dominant_freq.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LIBRARIES ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import mne\n",
    "import pandas as pd\n",
    "from mne_bids import BIDSPath, read_raw_bids, print_dir_tree, make_report, get_entities_from_fname\n",
    "import scipy.signal as signal\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.collections import LineCollection\n",
    "from matplotlib.colors import ListedColormap, BoundaryNorm\n",
    "import bids\n",
    "from mne.datasets.eegbci import standardize\n",
    "from fooof.utils import trim_spectrum\n",
    "#For plots outside jupiter\n",
    "get_ipython().run_line_magic(\"matplotlib\", \" qt \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DATASETS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Medellin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\antioquia\\\\derivatives\\\\sovaharmony\\\\sub-071\\\\eeg\\\\sub-071_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\antioquia\\\\derivatives\\\\sovaharmony\\\\sub-072\\\\eeg\\\\sub-072_task-rest_desc-reject_eeg.fif']\n"
     ]
    }
   ],
   "source": [
    "antioquia={\n",
    "'layout':{'extension':'.fif', 'suffix':'eeg', 'return_type':'filename'},\n",
    "    'ch_names':['FP1', 'FPZ', 'FP2', 'AF3', 'AF4', 'F7', 'F5', 'F3', 'F1', 'FZ', 'F2', 'F4', 'F6', 'F8', 'FC5', 'FC3', 'FC1', 'FCZ', 'FC2', 'FC4', 'FC6', 'T7', 'C5', 'C3', 'C1', 'CZ', 'C2', 'C4', 'C6', 'T8', 'TP7', 'CP5', 'CP3', 'CP1', 'CPZ', 'CP2', 'CP4', 'CP6', 'TP8', 'P7', 'P5', 'P3', 'P1', 'PZ', 'P2', 'P4', 'P6', 'P8', 'PO7', 'PO5', 'PO3', 'POZ', 'PO4', 'PO6', 'PO8', 'O1', 'OZ', 'O2'],\n",
    "    'path':\"D:/EEGs/PhD_datasets/BIDS/antioquia/\"\n",
    "}\n",
    "\n",
    "#Path of the BIDS folder\n",
    "bids_root = \"D:/EEGs/PhD_datasets/BIDS/antioquia/\"\n",
    "#Seleccionar solo EEG\n",
    "datatype = 'eeg'\n",
    "suffix = 'eeg'\n",
    "\n",
    "#Tarea\n",
    "task = 'rest' \n",
    "\n",
    "DATASET=antioquia #SI SE CAMBIA POR CALIFORNIA, SÍ SIRVE LA LINEA DE ABAJO\n",
    "\n",
    "layoutd = DATASET.get('layout', None)\n",
    "\n",
    "layout = bids.BIDSLayout(DATASET.get('path', None), derivatives=True)\n",
    "eegs = layout.get(**layoutd)\n",
    "eegs = [k for k in eegs if 'desc-reject' in k]\n",
    "\n",
    "print(len(eegs), eegs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "FOR STANDARD AVERAGED EPOCHS APPROACH - DF WAS CALCULATED OVER THE MEAN PSD VECTOR, PSD VARIABILITY WAS CALCULATED FROM THE EPOCHS PSD VECTORS IN R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%%time\n",
    "#eegs = ['D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\antioquia\\\\derivatives\\\\sovaharmony\\\\sub-001\\\\eeg\\\\sub-001_task-rest_desc-reject_eeg.fif']\n",
    "dict_list = []\n",
    "for eeg_file in eegs: #read and preload dataset\n",
    "    subject_info = layout.parse_file_entities(eeg_file)\n",
    "    session = subject_info.get('session')\n",
    "    subject = subject_info.get('subject')\n",
    "    bids_path = BIDSPath(subject=subject, session=session, task=task, root=bids_root, datatype='eeg')\n",
    "\n",
    "    data_preload = {\n",
    "        \"preload\": True\n",
    "    }\n",
    "    epochs = mne.read_epochs(eeg_file, preload = True)\n",
    "    standardize(epochs) #standardize ch_names\n",
    "    downsample = 500 #downsampling to 500Hz\n",
    "    epochs.resample(downsample)\n",
    "    sf = downsample\n",
    "    epochs_c = mne.concatenate_epochs(epochs_list = [epochs[:][range(44)]], add_offset=False, on_mismatch='raise', verbose=None)\n",
    "    epochs = epochs_c    \n",
    "    subject_info = layout.parse_file_entities(eeg_file)\n",
    "    session = subject_info.get('session')\n",
    "    subject = subject_info.get('subject')\n",
    "    nepochs,nchannels,npoints = epochs._data.shape\n",
    "    correct_channels = epochs.info['ch_names']\n",
    "    epochs.reorder_channels(correct_channels)\n",
    "    channels = epochs.info['ch_names']\n",
    "    for ch,ch_label in enumerate(channels):\n",
    "        features = {}\n",
    "        srate = 500\n",
    "        n_fft, n_overlap, n_per_seg= int(2*srate), int(srate), int(2*srate)\n",
    "        psd, freqs = mne.time_frequency.psd_array_welch(epochs.get_data()[:,ch,:], sfreq = srate, n_per_seg=n_per_seg, fmin=1, fmax=30, n_overlap = n_overlap, n_fft= n_fft, average='mean', verbose = False)\n",
    "        psds_mean = psd.mean(0)\n",
    "        psd = psds_mean\n",
    "        al_freqs, al_psd = trim_spectrum(freqs, psd, [4, 15])\n",
    "        icf_ind = np.argmax(np.abs(al_psd))\n",
    "        al_icf = al_freqs[icf_ind]\n",
    "        dominant_freq = al_icf\n",
    "        features['center'] = 'medellin'\n",
    "        features['subject'] = subject\n",
    "        features['channel'] = ch_label\n",
    "        features['df_ep'] = dominant_freq\n",
    "        dict_list.append(features)\n",
    "df = pd.DataFrame(dict_list)\n",
    "df.to_csv('D:/EEGs/PhD_datasets/BIDS/antioquia/derivatives/sovaharmony/df_medellin_5s_averaged.csv', index=False)\n",
    "\n",
    "#    df['dfv_ch'] = abs(df.df_ep - df.groupby('channel')['df_ep'].shift(periods=1)) \n",
    "#    df['dfv_ch'] = df['dfv_ch'].fillna(0)\n",
    "#    df2[\"dfv_mean\"] = df[\"dfv_ch\"].groupby(df[\"channel\"]).mean()\n",
    "#    df2 = pd.DataFrame(df2)\n",
    "#    dom_freq = pd.merge(df, df2, on=\"channel\")\n",
    "#    dominant.append(dom_freq)\n",
    "#    print('DOMINANT FREQUENCY DONE')\n",
    "#dominant_frequency = pd.DataFrame(dominant)\n",
    "# Loading epochs data\n",
    "\n",
    "# df2 = pd.read_csv('D:/escritorio/FDA/medellin/df_medellin_5s_no_overlapping.csv')\n",
    "# df2_filtered_epochs = df2[df2['epochs'] <= 28]\n",
    "# df2_filtered_epochs['dfv_ch'] = np.std(df2_filtered_epochs.groupby(['subject','channel'])['df_ep'])\n",
    "# \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "%%capture\n",
    "dominant = []\n",
    "dict_list = []\n",
    "for eeg_file in eegs: #read and preload dataset\n",
    "    subject_info = layout.parse_file_entities(eeg_file)\n",
    "    session = subject_info.get('session')\n",
    "    subject = subject_info.get('subject')\n",
    "    bids_path = BIDSPath(subject=subject, session=session, task=task, root=bids_root, datatype='eeg')\n",
    "\n",
    "    data_preload = {\n",
    "        \"preload\": True\n",
    "    }\n",
    "    epochs = mne.read_epochs(eeg_file, preload = True)\n",
    "    standardize(epochs) #standardize ch_names\n",
    "    downsample = 500 #downsampling to 500Hz\n",
    "    epochs.resample(downsample)\n",
    "    sf = downsample\n",
    "    print('BANDPASS FILTER 4 - 15 Hz & DOWNSAMPLE DONE')\n",
    "    subject_info = layout.parse_file_entities(eeg_file)\n",
    "    session = subject_info.get('session')\n",
    "    subject = subject_info.get('subject')\n",
    "    nepochs,nchannels,npoints = epochs._data.shape\n",
    "    correct_channels = epochs.info['ch_names']\n",
    "    epochs.reorder_channels(correct_channels)\n",
    "    channels = epochs.info['ch_names']\n",
    "    for ch,ch_label in enumerate(channels):\n",
    "        for ep in range(nepochs):\n",
    "            srate = downsample #This gets the sampling rate of the EEGLAB object\n",
    "            nfft, noverlap, nperseg = int(2*srate), int(srate), int(2*srate)\n",
    "            freqs, psd = signal.welch(epochs._data[ep,ch,:], fs=srate,  nfft=nfft, noverlap=noverlap, nperseg=nperseg, window='hamming', scaling='spectrum', average='mean') #This calculates PSD Welch\n",
    "            al_freqs, al_psd = trim_spectrum(freqs, psd, [4, 15])\n",
    "            icf_ind = np.argmax(np.abs(al_psd))\n",
    "            al_icf = al_freqs[icf_ind]\n",
    "            dominant_freq = al_icf\n",
    "            features = {}\n",
    "            features['center'] = 'medellin'\n",
    "            features['subject'] = subject\n",
    "            features['channel'] = ch_label\n",
    "            features['epoch'] = ep\n",
    "            features['df_ep'] = dominant_freq\n",
    "            dict_list.append(features)\n",
    "            df = pd.DataFrame(dict_list)\n",
    "            df['dfv_ch'] = abs(df.df_ep - df.groupby(['subject','channel'])['df_ep'].shift(periods=1))\n",
    "#    df['dfv_ch'] = abs(df.df_ep - df.groupby('channel')['df_ep'].shift(periods=1)) \n",
    "#    df['dfv_ch'] = df['dfv_ch'].fillna(0)\n",
    "#    df2[\"dfv_mean\"] = df[\"dfv_ch\"].groupby(df[\"channel\"]).mean()\n",
    "#    df2 = pd.DataFrame(df2)\n",
    "#    dom_freq = pd.merge(df, df2, on=\"channel\")\n",
    "#    dominant.append(dom_freq)\n",
    "#    print('DOMINANT FREQUENCY DONE')\n",
    "#dominant_frequency = pd.DataFrame(dominant)\n",
    "df['dfv_ch'] = df['dfv_ch'].fillna(0)\n",
    "df.to_csv('D:/EEGs/PhD_datasets/BIDS/antioquia/derivatives/sovaharmony/df_medellin_5s_no_overlapping.csv', index=False)\n",
    "print('CSV CREATED')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "%%capture\n",
    "dict_list = []\n",
    "for eeg_file in eegs: #read and preload dataset\n",
    "    subject_info = layout.parse_file_entities(eeg_file)\n",
    "    session = subject_info.get('session')\n",
    "    subject = subject_info.get('subject')\n",
    "    bids_path = BIDSPath(subject=subject, session=session, task=task, root=bids_root, datatype='eeg')\n",
    "\n",
    "    data_preload = {\n",
    "        \"preload\": True\n",
    "    }\n",
    "    epochs = mne.read_epochs(eeg_file, preload = True)\n",
    "    standardize(epochs) #standardize ch_names\n",
    "    downsample = 500 #downsampling to 500Hz\n",
    "    epochs.resample(downsample)\n",
    "    sf = downsample\n",
    "    print('BANDPASS FILTER 4 - 15 Hz & DOWNSAMPLE DONE')\n",
    "    subject_info = layout.parse_file_entities(eeg_file)\n",
    "    session = subject_info.get('session')\n",
    "    subject = subject_info.get('subject')\n",
    "    nepochs,nchannels,npoints = epochs._data.shape\n",
    "    correct_channels = epochs.info['ch_names']\n",
    "    epochs.reorder_channels(correct_channels)\n",
    "    channels = epochs.info['ch_names']\n",
    "    for ch,ch_label in enumerate(channels):\n",
    "        srate = downsample #This gets the sampling rate of the EEGLAB object\n",
    "        nfft, noverlap, nperseg = int(2*srate), int(srate), int(2*srate)\n",
    "        freqs, psd = signal.welch(epochs._data[:,ch,:], fs=srate,  nfft=nfft, noverlap=noverlap, nperseg=nperseg, window='hamming', scaling='spectrum', average='mean') #This calculates PSD Welch\n",
    "        al_freqs, al_psd = trim_spectrum(freqs, psd, [4, 15])\n",
    "        icf_ind = np.argmax(np.abs(al_psd))\n",
    "        al_icf = al_freqs[icf_ind]\n",
    "        dominant_freq = al_icf\n",
    "        features = {}\n",
    "        features['center'] = 'medellin'\n",
    "        features['subject'] = subject\n",
    "        features['channel'] = ch_label\n",
    "        features['df_ep'] = dominant_freq\n",
    "        dict_list.append(features)\n",
    "        df = pd.DataFrame(dict_list)\n",
    "        df['dfv_ch'] = abs(df.df_ep - df.groupby(['subject','channel'])['df_ep'].shift(periods=1))\n",
    "#    df['dfv_ch'] = abs(df.df_ep - df.groupby('channel')['df_ep'].shift(periods=1)) \n",
    "#    df['dfv_ch'] = df['dfv_ch'].fillna(0)\n",
    "#    df2[\"dfv_mean\"] = df[\"dfv_ch\"].groupby(df[\"channel\"]).mean()\n",
    "#    df2 = pd.DataFrame(df2)\n",
    "#    dom_freq = pd.merge(df, df2, on=\"channel\")\n",
    "#    dominant.append(dom_freq)\n",
    "#    print('DOMINANT FREQUENCY DONE')\n",
    "#dominant_frequency = pd.DataFrame(dominant)\n",
    "df.to_csv('D:/EEGs/PhD_datasets/BIDS/antioquia/derivatives/sovaharmony/df_medellin_5s_averaged.csv', index=False)\n",
    "print('CSV CREATED')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### California #####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31 ['D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-hc1\\\\ses-hc\\\\eeg\\\\sub-hc1_ses-hc_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-hc2\\\\ses-hc\\\\eeg\\\\sub-hc2_ses-hc_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-hc4\\\\ses-hc\\\\eeg\\\\sub-hc4_ses-hc_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-hc7\\\\ses-hc\\\\eeg\\\\sub-hc7_ses-hc_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-hc8\\\\ses-hc\\\\eeg\\\\sub-hc8_ses-hc_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-hc10\\\\ses-hc\\\\eeg\\\\sub-hc10_ses-hc_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-hc18\\\\ses-hc\\\\eeg\\\\sub-hc18_ses-hc_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-hc20\\\\ses-hc\\\\eeg\\\\sub-hc20_ses-hc_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-hc21\\\\ses-hc\\\\eeg\\\\sub-hc21_ses-hc_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-hc24\\\\ses-hc\\\\eeg\\\\sub-hc24_ses-hc_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-hc25\\\\ses-hc\\\\eeg\\\\sub-hc25_ses-hc_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-hc29\\\\ses-hc\\\\eeg\\\\sub-hc29_ses-hc_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-hc30\\\\ses-hc\\\\eeg\\\\sub-hc30_ses-hc_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-hc31\\\\ses-hc\\\\eeg\\\\sub-hc31_ses-hc_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-hc32\\\\ses-hc\\\\eeg\\\\sub-hc32_ses-hc_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-hc33\\\\ses-hc\\\\eeg\\\\sub-hc33_ses-hc_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-pd3\\\\ses-on\\\\eeg\\\\sub-pd3_ses-on_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-pd5\\\\ses-on\\\\eeg\\\\sub-pd5_ses-on_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-pd6\\\\ses-on\\\\eeg\\\\sub-pd6_ses-on_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-pd9\\\\ses-on\\\\eeg\\\\sub-pd9_ses-on_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-pd11\\\\ses-on\\\\eeg\\\\sub-pd11_ses-on_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-pd12\\\\ses-on\\\\eeg\\\\sub-pd12_ses-on_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-pd13\\\\ses-on\\\\eeg\\\\sub-pd13_ses-on_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-pd14\\\\ses-on\\\\eeg\\\\sub-pd14_ses-on_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-pd16\\\\ses-on\\\\eeg\\\\sub-pd16_ses-on_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-pd17\\\\ses-on\\\\eeg\\\\sub-pd17_ses-on_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-pd19\\\\ses-on\\\\eeg\\\\sub-pd19_ses-on_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-pd22\\\\ses-on\\\\eeg\\\\sub-pd22_ses-on_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-pd23\\\\ses-on\\\\eeg\\\\sub-pd23_ses-on_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-pd26\\\\ses-on\\\\eeg\\\\sub-pd26_ses-on_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\california\\\\derivatives\\\\sovaharmony\\\\sub-pd28\\\\ses-on\\\\eeg\\\\sub-pd28_ses-on_task-rest_desc-reject_eeg.fif']\n"
     ]
    }
   ],
   "source": [
    "california={\n",
    "'layout':{'extension':'.fif', 'session':['hc', 'on'], 'suffix':'eeg', 'return_type':'filename'},\n",
    "    'ch_names':['FP1', 'AF3', 'F7', 'F3', 'FC1', 'FC5', 'T7', 'C3', 'CP1', 'CP5', 'P7', 'P3', 'PZ', 'PO3', 'O1', 'OZ', 'O2', 'PO4', 'P4', 'P8', 'CP6', 'CP2', 'C4', 'T8', 'FC6', 'FC2', 'F4', 'F8', 'AF4', 'FP2', 'FZ', 'CZ'],\n",
    "    'path':\"D:/EEGs/PhD_datasets/BIDS/california/\"\n",
    "}\n",
    "\n",
    "#Path of the BIDS folder\n",
    "bids_root = \"D:/EEGs/PhD_datasets/BIDS/california/\"\n",
    "#Seleccionar solo EEG\n",
    "datatype = 'eeg'\n",
    "suffix = 'eeg'\n",
    "\n",
    "#Tarea\n",
    "task = 'rest' \n",
    "\n",
    "DATASET=california #DEFINE DATASET\n",
    "\n",
    "layoutd = DATASET.get('layout', None)\n",
    "\n",
    "layout = bids.BIDSLayout(DATASET.get('path', None), derivatives=True)\n",
    "eegs = layout.get(**layoutd)\n",
    "eegs = [k for k in eegs if 'desc-reject' in k]\n",
    "\n",
    "print(len(eegs), eegs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "%%capture\n",
    "dominant = []\n",
    "dict_list = []\n",
    "for eeg_file in eegs: #read and preload dataset\n",
    "    subject_info = layout.parse_file_entities(eeg_file)\n",
    "    session = subject_info.get('session')\n",
    "    subject = subject_info.get('subject')\n",
    "    bids_path = BIDSPath(subject=subject, session=session, task=task, root=bids_root, datatype='eeg')\n",
    "\n",
    "    data_preload = {\n",
    "        \"preload\": True\n",
    "    }\n",
    "    epochs = mne.read_epochs(eeg_file, preload = True)\n",
    "    standardize(epochs) #standardize ch_names\n",
    "    downsample = 500 #downsampling to 500Hz\n",
    "    epochs.resample(downsample)\n",
    "    sf = downsample\n",
    "    epochs_c = mne.concatenate_epochs(epochs_list = [epochs[:][range(28)]], add_offset=False, on_mismatch='raise', verbose=None)\n",
    "    epochs = epochs_c    \n",
    "    subject_info = layout.parse_file_entities(eeg_file)\n",
    "    session = subject_info.get('session')\n",
    "    subject = subject_info.get('subject')\n",
    "    nepochs,nchannels,npoints = epochs._data.shape\n",
    "    correct_channels = epochs.info['ch_names']\n",
    "    epochs.reorder_channels(correct_channels)\n",
    "    channels = epochs.info['ch_names']\n",
    "    for ch,ch_label in enumerate(channels):\n",
    "        srate = downsample #This gets the sampling rate of the EEGLAB object\n",
    "        nfft, noverlap, nperseg = int(2*srate), int(srate), int(2*srate)\n",
    "        freqs, psd = signal.welch(epochs._data[:,ch,:], fs=srate,  nfft=nfft, noverlap=noverlap, nperseg=nperseg, window='hamming', scaling='spectrum', average = 'mean') #This calculates PSD Welch\n",
    "        al_freqs, al_psd = trim_spectrum(freqs, psd, [4, 15])\n",
    "        icf_ind = np.argmax(np.abs(al_psd))\n",
    "        al_icf = al_freqs[icf_ind]\n",
    "        dominant_freq = al_icf\n",
    "        features = {}\n",
    "        features['center'] = 'california'\n",
    "        features['subject'] = subject\n",
    "        features['channel'] = ch_label\n",
    "        features['df_ep'] = dominant_freq\n",
    "        dict_list.append(features)\n",
    "df = pd.DataFrame(dict_list)\n",
    "#    df['dfv_ch'] = abs(df.df_ep - df.groupby('channel')['df_ep'].shift(periods=1)) \n",
    "#    df['dfv_ch'] = df['dfv_ch'].fillna(0)\n",
    "#    df2[\"dfv_mean\"] = df[\"dfv_ch\"].groupby(df[\"channel\"]).mean()\n",
    "#    df2 = pd.DataFrame(df2)\n",
    "#    dom_freq = pd.merge(df, df2, on=\"channel\")\n",
    "#    dominant.append(dom_freq)\n",
    "#    print('DOMINANT FREQUENCY DONE')\n",
    "#dominant_frequency = pd.DataFrame(dominant)\n",
    "# Loading epochs data\n",
    "df.to_csv('D:/EEGs/PhD_datasets/BIDS/california/derivatives/sovaharmony/df_california_5s_no_overlapping.csv', index=False)\n",
    "\n",
    "# df2 = pd.read_csv('D:/escritorio/FDA/medellin/df_medellin_5s_no_overlapping.csv')\n",
    "# df2_filtered_epochs = df2[df2['epochs'] <= 28]\n",
    "# df2_filtered_epochs['dfv_ch'] = np.std(df2_filtered_epochs.groupby(['subject','channel'])['df_ep'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%%time\n",
    "#eegs = ['D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\antioquia\\\\derivatives\\\\sovaharmony\\\\sub-001\\\\eeg\\\\sub-001_task-rest_desc-reject_eeg.fif']\n",
    "dict_list = []\n",
    "for eeg_file in eegs: #read and preload dataset\n",
    "    subject_info = layout.parse_file_entities(eeg_file)\n",
    "    session = subject_info.get('session')\n",
    "    subject = subject_info.get('subject')\n",
    "    bids_path = BIDSPath(subject=subject, session=session, task=task, root=bids_root, datatype='eeg')\n",
    "\n",
    "    data_preload = {\n",
    "        \"preload\": True\n",
    "    }\n",
    "    epochs = mne.read_epochs(eeg_file, preload = True)\n",
    "    standardize(epochs) #standardize ch_names\n",
    "    downsample = 500 #downsampling to 500Hz\n",
    "    epochs.resample(downsample)\n",
    "    sf = downsample\n",
    "    epochs_c = mne.concatenate_epochs(epochs_list = [epochs[:][range(27)]], add_offset=False, on_mismatch='raise', verbose=None)\n",
    "    epochs = epochs_c    \n",
    "    subject_info = layout.parse_file_entities(eeg_file)\n",
    "    session = subject_info.get('session')\n",
    "    subject = subject_info.get('subject')\n",
    "    nepochs,nchannels,npoints = epochs._data.shape\n",
    "    correct_channels = epochs.info['ch_names']\n",
    "    epochs.reorder_channels(correct_channels)\n",
    "    channels = epochs.info['ch_names']\n",
    "    for ch,ch_label in enumerate(channels):\n",
    "        features = {}\n",
    "        srate = 500\n",
    "        n_fft, n_overlap, n_per_seg= int(2*srate), int(srate), int(2*srate)\n",
    "        psd, freqs = mne.time_frequency.psd_array_welch(epochs.get_data()[:,ch,:], sfreq = srate, n_per_seg=n_per_seg, fmin=1, fmax=30, n_overlap = n_overlap, n_fft= n_fft, average='mean', verbose = False)\n",
    "        psds_mean = psd.mean(0)\n",
    "        psd = psds_mean\n",
    "        al_freqs, al_psd = trim_spectrum(freqs, psd, [4, 15])\n",
    "        icf_ind = np.argmax(np.abs(al_psd))\n",
    "        al_icf = al_freqs[icf_ind]\n",
    "        dominant_freq = al_icf\n",
    "        features['center'] = 'california'\n",
    "        features['subject'] = subject\n",
    "        features['channel'] = ch_label\n",
    "        features['df_ep'] = dominant_freq\n",
    "        dict_list.append(features)\n",
    "df = pd.DataFrame(dict_list)\n",
    "df.to_csv('D:/EEGs/PhD_datasets/BIDS/california/derivatives/sovaharmony/df_california_5s_averaged.csv', index=False)\n",
    "\n",
    "# df2 = pd.read_csv('D:/escritorio/FDA/medellin/df_medellin_5s_no_overlapping.csv')\n",
    "# df2_filtered_epochs = df2[df2['epochs'] <= 28]\n",
    "# df2_filtered_epochs['dfv_ch'] = np.std(df2_filtered_epochs.groupby(['subject','channel'])['df_ep'])\n",
    "# \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 12min 23s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "%%capture\n",
    "dominant = []\n",
    "dict_list = []\n",
    "for eeg_file in eegs: #read and preload dataset\n",
    "    subject_info = layout.parse_file_entities(eeg_file)\n",
    "    session = subject_info.get('session')\n",
    "    subject = subject_info.get('subject')\n",
    "    bids_path = BIDSPath(subject=subject, session=session, task=task, root=bids_root, datatype='eeg')\n",
    "\n",
    "    data_preload = {\n",
    "        \"preload\": True\n",
    "    }\n",
    "    epochs = mne.read_epochs(eeg_file, preload = True)\n",
    "    standardize(epochs) #standardize ch_names\n",
    "    downsample = 500 #downsampling to 500Hz\n",
    "    epochs.resample(downsample)\n",
    "    sf = downsample\n",
    "    subject_info = layout.parse_file_entities(eeg_file)\n",
    "    session = subject_info.get('session')\n",
    "    subject = subject_info.get('subject')\n",
    "    nepochs,nchannels,npoints = epochs._data.shape\n",
    "    correct_channels = epochs.info['ch_names']\n",
    "    epochs.reorder_channels(correct_channels)\n",
    "    channels = epochs.info['ch_names']\n",
    "    for ch,ch_label in enumerate(channels):\n",
    "        for ep in range(nepochs):\n",
    "            srate = downsample #This gets the sampling rate of the EEGLAB object\n",
    "            nfft, noverlap, nperseg = int(2.5*srate), int(1.25*srate), int(2.5*srate)\n",
    "            freqs, psd = signal.welch(epochs._data[ep,ch,:], fs=srate,  nfft=nfft, noverlap=noverlap, nperseg=nperseg, window='hamming', scaling='spectrum') #This calculates PSD Welch\n",
    "            al_freqs, al_psd = trim_spectrum(freqs, psd, [4, 15])\n",
    "            icf_ind = np.argmax(np.abs(al_psd))\n",
    "            al_icf = al_freqs[icf_ind]\n",
    "            dominant_freq = al_icf\n",
    "            features = {}\n",
    "            features['center'] = 'california'\n",
    "            features['subject'] = subject\n",
    "            features['channel'] = ch_label\n",
    "            features['epoch'] = ep\n",
    "            features['df_ep'] = dominant_freq\n",
    "            dict_list.append(features)\n",
    "            df = pd.DataFrame(dict_list)\n",
    "            df['dfv_ch'] = abs(df.df_ep - df.groupby(['subject','channel'])['df_ep'].shift(periods=1))\n",
    "#    df['dfv_ch'] = abs(df.df_ep - df.groupby('channel')['df_ep'].shift(periods=1)) \n",
    "#    df['dfv_ch'] = df['dfv_ch'].fillna(0)\n",
    "#    df2[\"dfv_mean\"] = df[\"dfv_ch\"].groupby(df[\"channel\"]).mean()\n",
    "#    df2 = pd.DataFrame(df2)\n",
    "#    dom_freq = pd.merge(df, df2, on=\"channel\")\n",
    "#    dominant.append(dom_freq)\n",
    "#    print('DOMINANT FREQUENCY DONE')\n",
    "#dominant_frequency = pd.DataFrame(dominant)\n",
    "df['dfv_ch'] = df['dfv_ch'].fillna(0)\n",
    "df.to_csv('D:/EEGs/PhD_datasets/BIDS/california/derivatives/sovaharmony/df_california_5s_no_overlapping.csv', index=False)\n",
    "print('CSV CREATED')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Finland ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38 ['D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-001\\\\eeg\\\\sub-001_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-002\\\\eeg\\\\sub-002_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-003\\\\eeg\\\\sub-003_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-004\\\\eeg\\\\sub-004_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-005\\\\eeg\\\\sub-005_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-006\\\\eeg\\\\sub-006_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-007\\\\eeg\\\\sub-007_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-008\\\\eeg\\\\sub-008_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-009\\\\eeg\\\\sub-009_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-010\\\\eeg\\\\sub-010_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-011\\\\eeg\\\\sub-011_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-012\\\\eeg\\\\sub-012_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-013\\\\eeg\\\\sub-013_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-014\\\\eeg\\\\sub-014_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-015\\\\eeg\\\\sub-015_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-016\\\\eeg\\\\sub-016_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-017\\\\eeg\\\\sub-017_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-018\\\\eeg\\\\sub-018_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-019\\\\eeg\\\\sub-019_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-020\\\\eeg\\\\sub-020_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-021\\\\eeg\\\\sub-021_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-022\\\\eeg\\\\sub-022_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-023\\\\eeg\\\\sub-023_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-024\\\\eeg\\\\sub-024_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-025\\\\eeg\\\\sub-025_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-026\\\\eeg\\\\sub-026_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-027\\\\eeg\\\\sub-027_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-028\\\\eeg\\\\sub-028_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-030\\\\eeg\\\\sub-030_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-031\\\\eeg\\\\sub-031_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-032\\\\eeg\\\\sub-032_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-033\\\\eeg\\\\sub-033_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-034\\\\eeg\\\\sub-034_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-035\\\\eeg\\\\sub-035_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-036\\\\eeg\\\\sub-036_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-037\\\\eeg\\\\sub-037_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-038\\\\eeg\\\\sub-038_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\finland\\\\derivatives\\\\sovaharmony\\\\sub-039\\\\eeg\\\\sub-039_task-rest_desc-reject_eeg.fif']\n"
     ]
    }
   ],
   "source": [
    "finland={\n",
    "'layout':{'extension':'.fif', 'suffix':'eeg', 'return_type':'filename'},\n",
    "    'ch_names':['FP1', 'FP2', 'F3', 'F4', 'C3', 'C4', 'P3', 'P4', 'O1', 'O2', 'F7', 'F8', 'T7', 'T8', 'P7', 'P8', 'FZ', 'CZ', 'PZ', 'IZ', 'FC1', 'FC2', 'CP1', 'CP2', 'FC5', 'FC6', 'CP5', 'CP6', 'FT9', 'FT10', 'TP9', 'TP10', 'F1', 'F2', 'C1', 'C2', 'P1', 'P2', 'AF3', 'AF4', 'FC3', 'FC4', 'CP3', 'CP4', 'PO3', 'PO4', 'F5', 'F6', 'C5', 'C6', 'P5', 'P6', 'AF7', 'AF8', 'FT7', 'FT8', 'TP7', 'TP8', 'PO7', 'PO8', 'FPZ', 'CPZ', 'POZ', 'OZ'],\n",
    "    'path':\"D:/EEGs/PhD_datasets/BIDS/finland/\"\n",
    "}\n",
    "\n",
    "#Path of the BIDS folder\n",
    "bids_root = \"D:/EEGs/PhD_datasets/BIDS/finland/\"\n",
    "#Seleccionar solo EEG\n",
    "datatype = 'eeg'\n",
    "suffix = 'eeg'\n",
    "\n",
    "#Tarea\n",
    "task = 'rest' \n",
    "\n",
    "DATASET=finland #DEFINE DATASET\n",
    "\n",
    "layoutd = DATASET.get('layout', None)\n",
    "\n",
    "layout = bids.BIDSLayout(DATASET.get('path', None), derivatives=True)\n",
    "eegs = layout.get(**layoutd)\n",
    "eegs = [k for k in eegs if 'desc-reject' in k]\n",
    "\n",
    "print(len(eegs), eegs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%%time\n",
    "dominant = []\n",
    "dict_list = []\n",
    "for eeg_file in eegs: #read and preload dataset\n",
    "    subject_info = layout.parse_file_entities(eeg_file)\n",
    "    session = subject_info.get('session')\n",
    "    subject = subject_info.get('subject')\n",
    "    bids_path = BIDSPath(subject=subject, session=session, task=task, root=bids_root, datatype='eeg')\n",
    "\n",
    "    data_preload = {\n",
    "        \"preload\": True\n",
    "    }\n",
    "    epochs = mne.read_epochs(eeg_file, preload = True)\n",
    "    standardize(epochs) #standardize ch_names\n",
    "    downsample = 500 #downsampling to 500Hz\n",
    "    epochs.resample(downsample)\n",
    "    sf = downsample\n",
    "    print('BANDPASS FILTER 4-15 HZ & DOWNSAMPLE DONE')\n",
    "    subject_info = layout.parse_file_entities(eeg_file)\n",
    "    session = subject_info.get('session')\n",
    "    subject = subject_info.get('subject')\n",
    "    nepochs,nchannels,npoints = epochs._data.shape\n",
    "    correct_channels = epochs.info['ch_names']\n",
    "    epochs.reorder_channels(correct_channels)\n",
    "    channels = epochs.info['ch_names']\n",
    "    for ch,ch_label in enumerate(channels):\n",
    "            srate = downsample #This gets the sampling rate of the EEGLAB object\n",
    "            nfft, noverlap, nperseg = int(2.5*srate), int(1.25*srate), int(2.5*srate)\n",
    "            freqs, psd = signal.welch(epochs._data[ep,ch,:], fs=srate,  nfft=nfft, noverlap=noverlap, nperseg=nperseg, window='hamming', scaling='spectrum') #This calculates PSD Welch\n",
    "            al_freqs, al_psd = trim_spectrum(freqs, psd, [4, 15])\n",
    "            icf_ind = np.argmax(np.abs(al_psd))\n",
    "            al_icf = al_freqs[icf_ind]\n",
    "            dominant_freq = al_icf\n",
    "            features = {}\n",
    "            features['center'] = 'finland'\n",
    "            features['subject'] = subject\n",
    "            features['channel'] = ch_label\n",
    "            features['df_ep'] = dominant_freq\n",
    "            dict_list.append(features)\n",
    "            df = pd.DataFrame(dict_list)\n",
    "            df['dfv_ch'] = abs(df.df_ep - df.groupby(['subject','channel'])['df_ep'].shift(periods=1))\n",
    "#    df['dfv_ch'] = abs(df.df_ep - df.groupby('channel')['df_ep'].shift(periods=1)) \n",
    "#    df['dfv_ch'] = df['dfv_ch'].fillna(0)\n",
    "#    df2[\"dfv_mean\"] = df[\"dfv_ch\"].groupby(df[\"channel\"]).mean()\n",
    "#    df2 = pd.DataFrame(df2)\n",
    "#    dom_freq = pd.merge(df, df2, on=\"channel\")\n",
    "#    dominant.append(dom_freq)\n",
    "#    print('DOMINANT FREQUENCY DONE')\n",
    "#dominant_frequency = pd.DataFrame(dominant)\n",
    "df['dfv_ch'] = df['dfv_ch'].fillna(0)\n",
    "df.to_csv('D:/EEGs/PhD_datasets/BIDS/finland/derivatives/sovaharmony/df_finland_5s_no_overlapping.csv', index=False)\n",
    "print('CSV CREATED')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%%time\n",
    "#eegs = ['D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\antioquia\\\\derivatives\\\\sovaharmony\\\\sub-001\\\\eeg\\\\sub-001_task-rest_desc-reject_eeg.fif']\n",
    "dict_list = []\n",
    "for eeg_file in eegs: #read and preload dataset\n",
    "    subject_info = layout.parse_file_entities(eeg_file)\n",
    "    session = subject_info.get('session')\n",
    "    subject = subject_info.get('subject')\n",
    "    bids_path = BIDSPath(subject=subject, session=session, task=task, root=bids_root, datatype='eeg')\n",
    "\n",
    "    data_preload = {\n",
    "        \"preload\": True\n",
    "    }\n",
    "    epochs = mne.read_epochs(eeg_file, preload = True)\n",
    "    standardize(epochs) #standardize ch_names\n",
    "    downsample = 500 #downsampling to 500Hz\n",
    "    epochs.resample(downsample)\n",
    "    sf = downsample\n",
    "    epochs_c = mne.concatenate_epochs(epochs_list = [epochs[:][range(18)]], add_offset=False, on_mismatch='raise', verbose=None)\n",
    "    epochs = epochs_c    \n",
    "    subject_info = layout.parse_file_entities(eeg_file)\n",
    "    session = subject_info.get('session')\n",
    "    subject = subject_info.get('subject')\n",
    "    nepochs,nchannels,npoints = epochs._data.shape\n",
    "    correct_channels = epochs.info['ch_names']\n",
    "    epochs.reorder_channels(correct_channels)\n",
    "    channels = epochs.info['ch_names']\n",
    "    for ch,ch_label in enumerate(channels):\n",
    "        features = {}\n",
    "        srate = 500\n",
    "        n_fft, n_overlap, n_per_seg= int(2*srate), int(srate), int(2*srate)\n",
    "        psd, freqs = mne.time_frequency.psd_array_welch(epochs.get_data()[:,ch,:], sfreq = srate, n_per_seg=n_per_seg, fmin=1, fmax=30, n_overlap = n_overlap, n_fft= n_fft, average='mean', verbose = False)\n",
    "        psds_mean = psd.mean(0)\n",
    "        psd = psds_mean\n",
    "        al_freqs, al_psd = trim_spectrum(freqs, psd, [4, 15])\n",
    "        icf_ind = np.argmax(np.abs(al_psd))\n",
    "        al_icf = al_freqs[icf_ind]\n",
    "        dominant_freq = al_icf\n",
    "        features['center'] = 'finland'\n",
    "        features['subject'] = subject\n",
    "        features['channel'] = ch_label\n",
    "        features['df_ep'] = dominant_freq\n",
    "        dict_list.append(features)\n",
    "df = pd.DataFrame(dict_list)\n",
    "df.to_csv('D:/EEGs/PhD_datasets/BIDS/finland/derivatives/sovaharmony/df_finland_5s_averaged.csv', index=False)\n",
    "\n",
    "# df2 = pd.read_csv('D:/escritorio/FDA/medellin/df_medellin_5s_no_overlapping.csv')\n",
    "# df2_filtered_epochs = df2[df2['epochs'] <= 28]\n",
    "# df2_filtered_epochs['dfv_ch'] = np.std(df2_filtered_epochs.groupby(['subject','channel'])['df_ep'])\n",
    "# \n",
    "\n",
    "\n",
    "# df.to_csv('D:/EEGs/PhD_datasets/BIDS/finland/derivatives/sovaharmony/df_finland_5s_averaged.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-001\\eeg\\sub-001_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-001\\eeg\\sub-001_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "25 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-002\\eeg\\sub-002_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-002\\eeg\\sub-002_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "25 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-003\\eeg\\sub-003_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-003\\eeg\\sub-003_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "25 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-004\\eeg\\sub-004_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-004\\eeg\\sub-004_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "24 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-005\\eeg\\sub-005_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-005\\eeg\\sub-005_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "27 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-006\\eeg\\sub-006_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-006\\eeg\\sub-006_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "26 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-007\\eeg\\sub-007_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-007\\eeg\\sub-007_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "24 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-008\\eeg\\sub-008_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-008\\eeg\\sub-008_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "27 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-009\\eeg\\sub-009_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-009\\eeg\\sub-009_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "26 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-010\\eeg\\sub-010_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-010\\eeg\\sub-010_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "25 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-011\\eeg\\sub-011_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-011\\eeg\\sub-011_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "24 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-012\\eeg\\sub-012_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-012\\eeg\\sub-012_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "25 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-013\\eeg\\sub-013_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-013\\eeg\\sub-013_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "24 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-014\\eeg\\sub-014_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-014\\eeg\\sub-014_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "28 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-015\\eeg\\sub-015_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-015\\eeg\\sub-015_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "31 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-016\\eeg\\sub-016_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-016\\eeg\\sub-016_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "22 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-017\\eeg\\sub-017_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-017\\eeg\\sub-017_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "23 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-018\\eeg\\sub-018_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-018\\eeg\\sub-018_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "25 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-019\\eeg\\sub-019_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-019\\eeg\\sub-019_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "26 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-020\\eeg\\sub-020_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-020\\eeg\\sub-020_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "26 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-021\\eeg\\sub-021_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-021\\eeg\\sub-021_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "26 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-022\\eeg\\sub-022_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-022\\eeg\\sub-022_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "25 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-023\\eeg\\sub-023_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-023\\eeg\\sub-023_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "23 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-024\\eeg\\sub-024_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-024\\eeg\\sub-024_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "24 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-025\\eeg\\sub-025_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-025\\eeg\\sub-025_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "29 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-026\\eeg\\sub-026_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-026\\eeg\\sub-026_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "25 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-027\\eeg\\sub-027_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-027\\eeg\\sub-027_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "26 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-028\\eeg\\sub-028_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-028\\eeg\\sub-028_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "27 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-030\\eeg\\sub-030_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-030\\eeg\\sub-030_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "24 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-031\\eeg\\sub-031_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-031\\eeg\\sub-031_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "32 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-032\\eeg\\sub-032_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-032\\eeg\\sub-032_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "24 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-033\\eeg\\sub-033_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-033\\eeg\\sub-033_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "24 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-034\\eeg\\sub-034_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-034\\eeg\\sub-034_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "21 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-035\\eeg\\sub-035_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-035\\eeg\\sub-035_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "25 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-036\\eeg\\sub-036_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-036\\eeg\\sub-036_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "25 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-037\\eeg\\sub-037_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-037\\eeg\\sub-037_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "27 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-038\\eeg\\sub-038_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-038\\eeg\\sub-038_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "24 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-039\\eeg\\sub-039_task-rest_desc-reject_eeg.fif ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/2405570582.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\finland\\derivatives\\sovaharmony\\sub-039\\eeg\\sub-039_task-rest_desc-reject_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.00 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "24 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 1 - 30 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 1.00\n",
      "- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)\n",
      "- Upper passband edge: 30.00 Hz\n",
      "- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)\n",
      "- Filter length: 1651 samples (3.302 sec)\n",
      "\n",
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n",
      "CSV CREATED\n"
     ]
    }
   ],
   "source": [
    "    dict_list = []\n",
    "    for eeg_file in eegs: #read and preload dataset\n",
    "        subject_info = layout.parse_file_entities(eeg_file)\n",
    "        session = subject_info.get('session')\n",
    "        subject = subject_info.get('subject')\n",
    "        bids_path = BIDSPath(subject=subject, session=session, task=task, root=bids_root, datatype='eeg')\n",
    "\n",
    "        data_preload = {\n",
    "            \"preload\": True\n",
    "        }\n",
    "        epochs = mne.read_epochs(eeg_file, preload = True)\n",
    "        standardize(epochs) #standardize ch_names\n",
    "        epochs = epochs.filter(l_freq=1, h_freq=30) #bandpassing 1-30Hz\n",
    "        downsample = 500 #downsampling to 500Hz\n",
    "        epochs.resample(downsample)\n",
    "        sf = downsample\n",
    "        print('BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE')\n",
    "        subject_info = layout.parse_file_entities(eeg_file)\n",
    "        session = subject_info.get('session')\n",
    "        subject = subject_info.get('subject')\n",
    "        nepochs,nchannels,npoints = epochs._data.shape\n",
    "        correct_channels = epochs.info['ch_names']\n",
    "        epochs.reorder_channels(correct_channels)\n",
    "        channels = epochs.info['ch_names']\n",
    "        for ch,ch_label in enumerate(channels):\n",
    "            for ep in range(nepochs):\n",
    "                features = {}\n",
    "                features['center'] = 'finland'\n",
    "                features['subject'] = subject\n",
    "                features['channel'] = ch_label\n",
    "                features['epoch'] = ep\n",
    "                features['df_ep'] = peak_freq(epochs.get_data()[ep,ch,:], sf, [4, 15],'multitaper')\n",
    "                dict_list.append(features)\n",
    "                df = pd.DataFrame(dict_list)\n",
    "                df['dfv_ch'] = abs(df.df_ep - df.groupby(['subject','channel'])['df_ep'].shift(periods=1))\n",
    "    df['dfv_ch'] = df['dfv_ch'].fillna(0)\n",
    "print('CSV CREATED')\n",
    "df.to_csv('D:/EEGs/PhD_datasets/BIDS/finland/derivatives/sovaharmony/df_mt_finland_5s_no_overlapping.csv', index=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Iowa ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28 ['D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-001\\\\eeg\\\\sub-001_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-002\\\\eeg\\\\sub-002_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-003\\\\eeg\\\\sub-003_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-004\\\\eeg\\\\sub-004_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-005\\\\eeg\\\\sub-005_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-006\\\\eeg\\\\sub-006_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-007\\\\eeg\\\\sub-007_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-008\\\\eeg\\\\sub-008_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-009\\\\eeg\\\\sub-009_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-010\\\\eeg\\\\sub-010_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-011\\\\eeg\\\\sub-011_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-012\\\\eeg\\\\sub-012_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-013\\\\eeg\\\\sub-013_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-014\\\\eeg\\\\sub-014_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-015\\\\eeg\\\\sub-015_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-016\\\\eeg\\\\sub-016_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-017\\\\eeg\\\\sub-017_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-018\\\\eeg\\\\sub-018_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-019\\\\eeg\\\\sub-019_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-020\\\\eeg\\\\sub-020_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-021\\\\eeg\\\\sub-021_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-022\\\\eeg\\\\sub-022_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-023\\\\eeg\\\\sub-023_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-024\\\\eeg\\\\sub-024_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-025\\\\eeg\\\\sub-025_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-026\\\\eeg\\\\sub-026_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-027\\\\eeg\\\\sub-027_task-rest_desc-reject_eeg.fif', 'D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\iowa\\\\derivatives\\\\sovaharmony\\\\sub-028\\\\eeg\\\\sub-028_task-rest_desc-reject_eeg.fif']\n"
     ]
    }
   ],
   "source": [
    "iowa={\n",
    "'layout':{'extension':'.fif', 'suffix':'eeg', 'return_type':'filename'},\n",
    "    'ch_names':['T8', 'CP3', 'T7', 'C4', 'C5', 'FT8', 'F6', 'FC5', 'FC3', 'C2', 'TP9', 'F7', 'C6', 'CP6', 'F2', 'TP7', 'PO7', 'FCZ', 'O2', 'P2', 'CZ', 'F1', 'P6', 'AF8', 'C3', 'AF4', 'CPZ', 'F5', 'CP1', 'O1', 'P8', 'FC6', 'FC2', 'P4', 'F8', 'FP2', 'AF3', 'POZ', 'FT10', 'P1', 'FC1', 'TP10', 'FP1', 'AF7', 'PO8', 'TP8', 'F4', 'FZ', 'AFZ', 'OZ', 'CP4', 'P3', 'FT7', 'CP5', 'P5', 'P7', 'F3', 'FC4', 'CP2', 'C1'],\n",
    "    'path':\"D:/EEGs/PhD_datasets/BIDS/iowa/\"\n",
    "}\n",
    "\n",
    "#Path of the BIDS folder\n",
    "bids_root = \"D:/EEGs/PhD_datasets/BIDS/iowa/\"\n",
    "#Seleccionar solo EEG\n",
    "datatype = 'eeg'\n",
    "suffix = 'eeg'\n",
    "\n",
    "#Tarea\n",
    "task = 'rest' \n",
    "\n",
    "DATASET=iowa #DEFINE DATASET\n",
    "\n",
    "layoutd = DATASET.get('layout', None)\n",
    "\n",
    "layout = bids.BIDSLayout(DATASET.get('path', None), derivatives=True)\n",
    "eegs = layout.get(**layoutd)\n",
    "eegs = [k for k in eegs if 'desc-reject' in k]\n",
    "\n",
    "print(len(eegs), eegs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%%time\n",
    "#%%capture\n",
    "dominant = []\n",
    "dict_list = []\n",
    "for eeg_file in eegs: #read and preload dataset\n",
    "    subject_info = layout.parse_file_entities(eeg_file)\n",
    "    session = subject_info.get('session')\n",
    "    subject = subject_info.get('subject')\n",
    "    bids_path = BIDSPath(subject=subject, session=session, task=task, root=bids_root, datatype='eeg')\n",
    "\n",
    "    data_preload = {\n",
    "        \"preload\": True\n",
    "    }\n",
    "    epochs = mne.read_epochs(eeg_file, preload = True)\n",
    "    standardize(epochs) #standardize ch_names\n",
    "    downsample = 500 #downsampling to 500Hz\n",
    "    epochs.resample(downsample)\n",
    "    sf = downsample\n",
    "    subject_info = layout.parse_file_entities(eeg_file)\n",
    "    session = subject_info.get('session')\n",
    "    subject = subject_info.get('subject')\n",
    "    nepochs,nchannels,npoints = epochs._data.shape\n",
    "    correct_channels = epochs.info['ch_names']\n",
    "    epochs.reorder_channels(correct_channels)\n",
    "    channels = epochs.info['ch_names']\n",
    "    for ch,ch_label in enumerate(channels):\n",
    "        for ep in range(nepochs):\n",
    "            srate = downsample #This gets the sampling rate of the EEGLAB object\n",
    "            nfft, noverlap, nperseg = int(2.5*srate), int(1.25*srate), int(2.5*srate)\n",
    "            freqs, psd = signal.welch(epochs._data[ep,ch,:], fs=srate,  nfft=nfft, noverlap=noverlap, nperseg=nperseg, window='hamming', scaling='spectrum') #This calculates PSD Welch\n",
    "            al_freqs, al_psd = trim_spectrum(freqs, psd, [4, 15])\n",
    "            icf_ind = np.argmax(np.abs(al_psd))\n",
    "            al_icf = al_freqs[icf_ind]\n",
    "            dominant_freq = al_icf\n",
    "            features = {}\n",
    "            features['center'] = 'iowa'\n",
    "            features['subject'] = subject\n",
    "            features['channel'] = ch_label\n",
    "            features['epoch'] = ep\n",
    "            features['df_ep'] = dominant_freq\n",
    "            dict_list.append(features)\n",
    "            df = pd.DataFrame(dict_list)\n",
    "            df['dfv_ch'] = abs(df.df_ep - df.groupby(['subject','channel'])['df_ep'].shift(periods=1))\n",
    "#    df['dfv_ch'] = abs(df.df_ep - df.groupby('channel')['df_ep'].shift(periods=1)) \n",
    "#    df['dfv_ch'] = df['dfv_ch'].fillna(0)\n",
    "#    df2[\"dfv_mean\"] = df[\"dfv_ch\"].groupby(df[\"channel\"]).mean()\n",
    "#    df2 = pd.DataFrame(df2)\n",
    "#    dom_freq = pd.merge(df, df2, on=\"channel\")\n",
    "#    dominant.append(dom_freq)\n",
    "#    print('DOMINANT FREQUENCY DONE')\n",
    "#dominant_frequency = pd.DataFrame(dominant)\n",
    "df['dfv_ch'] = df['dfv_ch'].fillna(0)\n",
    "df.to_csv('D:/EEGs/PhD_datasets/BIDS/iowa/derivatives/sovaharmony/df_iowa_5s_no_overlapping.csv', index=False)\n",
    "print('CSV CREATED')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "%%time\n",
    "#eegs = ['D:\\\\EEGs\\\\PhD_datasets\\\\BIDS\\\\antioquia\\\\derivatives\\\\sovaharmony\\\\sub-001\\\\eeg\\\\sub-001_task-rest_desc-reject_eeg.fif']\n",
    "dict_list = []\n",
    "for eeg_file in eegs: #read and preload dataset\n",
    "    subject_info = layout.parse_file_entities(eeg_file)\n",
    "    session = subject_info.get('session')\n",
    "    subject = subject_info.get('subject')\n",
    "    bids_path = BIDSPath(subject=subject, session=session, task=task, root=bids_root, datatype='eeg')\n",
    "\n",
    "    data_preload = {\n",
    "        \"preload\": True\n",
    "    }\n",
    "    epochs = mne.read_epochs(eeg_file, preload = True)\n",
    "    standardize(epochs) #standardize ch_names\n",
    "    downsample = 500 #downsampling to 500Hz\n",
    "    epochs.resample(downsample)\n",
    "    sf = downsample\n",
    "    epochs_c = mne.concatenate_epochs(epochs_list = [epochs[:][range(16)]], add_offset=False, on_mismatch='raise', verbose=None)\n",
    "    epochs = epochs_c    \n",
    "    subject_info = layout.parse_file_entities(eeg_file)\n",
    "    session = subject_info.get('session')\n",
    "    subject = subject_info.get('subject')\n",
    "    nepochs,nchannels,npoints = epochs._data.shape\n",
    "    correct_channels = epochs.info['ch_names']\n",
    "    epochs.reorder_channels(correct_channels)\n",
    "    channels = epochs.info['ch_names']\n",
    "    for ch,ch_label in enumerate(channels):\n",
    "        features = {}\n",
    "        srate = 500\n",
    "        n_fft, n_overlap, n_per_seg= int(2*srate), int(srate), int(2*srate)\n",
    "        psd, freqs = mne.time_frequency.psd_array_welch(epochs.get_data()[:,ch,:], sfreq = srate, n_per_seg=n_per_seg, fmin=1, fmax=30, n_overlap = n_overlap, n_fft= n_fft, average='mean', verbose = False)\n",
    "        psds_mean = psd.mean(0)\n",
    "        psd = psds_mean\n",
    "        al_freqs, al_psd = trim_spectrum(freqs, psd, [4, 15])\n",
    "        icf_ind = np.argmax(np.abs(al_psd))\n",
    "        al_icf = al_freqs[icf_ind]\n",
    "        dominant_freq = al_icf\n",
    "        features['center'] = 'iowa'\n",
    "        features['subject'] = subject\n",
    "        features['channel'] = ch_label\n",
    "        features['df_ep'] = dominant_freq\n",
    "        dict_list.append(features)\n",
    "df = pd.DataFrame(dict_list)\n",
    "df.to_csv('D:/EEGs/PhD_datasets/BIDS/iowa/derivatives/sovaharmony/df_iowa_5s_averaged.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MULTITAPER: ALPHA PEAK FREQUENCY "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def peak_freq(data, sf, band, method='multitaper', window_sec=None, relative=False):\n",
    "    \"\"\"Compute the average power of the signal x in a specific frequency band.\n",
    "\n",
    "    Requires MNE-Python >= 0.14.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    data : 1d-array\n",
    "      Input signal in the time-domain.\n",
    "    sf : float\n",
    "      Sampling frequency of the data.\n",
    "    band : list\n",
    "      ONLY FOR WELCH METHOD. Lower and upper frequencies of the band of interest.\n",
    "    method : string\n",
    "      Periodogram method: 'welch' or 'multitaper'\n",
    "    window_sec : float\n",
    "      Length of each window in seconds. Useful only if method == 'welch'.\n",
    "      If None, window_sec = (1 / min(band)) * 2.\n",
    "    relative : boolean\n",
    "      If True, return the relative power (= divided by the total power of the signal).\n",
    "      If False (default), return the absolute power.\n",
    "\n",
    "    Return\n",
    "    ------\n",
    "    bp : float\n",
    "      Absolute or relative band power.\n",
    "    \"\"\"\n",
    "    from scipy.signal import welch\n",
    "    from scipy.integrate import simps\n",
    "    from mne.time_frequency import psd_array_multitaper\n",
    "\n",
    "    band = np.asarray(band)\n",
    "    low, high = band\n",
    "\n",
    "    # Compute the modified periodogram (Welch)\n",
    "    if method == 'welch':\n",
    "        if window_sec is not None:\n",
    "            nperseg = window_sec * sf\n",
    "        else:\n",
    "            nperseg = (2 / low) * sf\n",
    "\n",
    "        freqs, psd = welch(data, sf, nperseg=nperseg)\n",
    "\n",
    "    elif method == 'multitaper':\n",
    "        psd, freqs = psd_array_multitaper(data, sf, fmin = low, fmax = high, bandwidth = 2, adaptive=True,\n",
    "                                          normalization='full', low_bias=True, verbose=0) ## Taper lenght is equal to 2Hz \n",
    "                                                                        ##### as this \"is the optimal value to observe the extended alpha band\", in my opinion might be quite large peak bandwidth\n",
    "    \n",
    "    \n",
    "    al_freqs, al_psd = trim_spectrum(freqs, psd, [4, 15])\n",
    "    icf_ind = np.argmax(np.abs(al_psd))\n",
    "    al_icf = al_freqs[icf_ind]    \n",
    "\n",
    "    return al_icf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enter the research center field in the features file, line of code number 28\n",
    "Enter the file name and path for the features file, line of code number 37\n",
    "The \"band\" parameter in the peak_freq function is defind as 4 - 14 Hz (extended alpha band)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading D:\\EEGs\\PhD_datasets\\BIDS\\california\\derivatives\\sovaharmony\\sub-hc1\\ses-hc\\eeg\\sub-hc1_ses-hc_task-rest_desc-norm_eeg.fif ...\n",
      "Isotrak not found\n",
      "    Found the data of interest:\n",
      "        t =       0.00 ...    4998.05 ms\n",
      "        0 CTF compensation matrices available\n",
      "Not setting metadata\n",
      "Not setting metadata\n",
      "31 matching events found\n",
      "No baseline correction applied\n",
      "0 projection items activated\n",
      "Setting up band-pass filter from 4 - 15 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 4.00\n",
      "- Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 3.00 Hz)\n",
      "- Upper passband edge: 15.00 Hz\n",
      "- Upper transition bandwidth: 3.75 Hz (-6 dB cutoff frequency: 16.88 Hz)\n",
      "- Filter length: 845 samples (1.650 sec)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\acer\\AppData\\Local\\Temp/ipykernel_3688/3618531071.py:11: RuntimeWarning: This filename (D:\\EEGs\\PhD_datasets\\BIDS\\california\\derivatives\\sovaharmony\\sub-hc1\\ses-hc\\eeg\\sub-hc1_ses-hc_task-rest_desc-norm_eeg.fif) does not conform to MNE naming conventions. All epochs files should end with -epo.fif, -epo.fif.gz, _epo.fif or _epo.fif.gz\n",
      "  epochs = mne.read_epochs(eeg_file, preload = True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE\n"
     ]
    }
   ],
   "source": [
    "dict_list = []\n",
    "for eeg_file in eegs: #read and preload dataset\n",
    "    subject_info = layout.parse_file_entities(eeg_file)\n",
    "    session = subject_info.get('session')\n",
    "    subject = subject_info.get('subject')\n",
    "    bids_path = BIDSPath(subject=subject, session=session, task=task, root=bids_root, datatype='eeg')\n",
    "\n",
    "    data_preload = {\n",
    "        \"preload\": True\n",
    "    }\n",
    "    epochs = mne.read_epochs(eeg_file, preload = True)\n",
    "    standardize(epochs) #standardize ch_names\n",
    "    epochs = epochs.filter(l_freq=1, h_freq=30) #bandpassing 1-30Hz\n",
    "    downsample = 500 #downsampling to 500Hz\n",
    "    epochs.resample(downsample)\n",
    "    sf = downsample\n",
    "    print('BANDPASS FILTER 1-30 HZ & DOWNSAMPLE DONE')\n",
    "    subject_info = layout.parse_file_entities(eeg_file)\n",
    "    session = subject_info.get('session')\n",
    "    subject = subject_info.get('subject')\n",
    "    nepochs,nchannels,npoints = epochs._data.shape\n",
    "    correct_channels = epochs.info['ch_names']\n",
    "    epochs.reorder_channels(correct_channels)\n",
    "    channels = epochs.info['ch_names']\n",
    "    for ch,ch_label in enumerate(channels):\n",
    "        for ep in range(nepochs):\n",
    "            features = {}\n",
    "            features['center'] = 'california'\n",
    "            features['subject'] = subject\n",
    "            features['channel'] = ch_label\n",
    "            features['epoch'] = ep\n",
    "            features['df_ep'] = peak_freq(epochs.get_data()[ep,ch,:], sf, [4, 14],'multitaper')\n",
    "            dict_list.append(features)\n",
    "            df = pd.DataFrame(dict_list)\n",
    "            df['dfv_ch'] = abs(df.df_ep - df.groupby(['subject','channel'])['df_ep'].shift(periods=1))\n",
    "df['dfv_ch'] = df['dfv_ch'].fillna(0)\n",
    "df.to_csv('D:/EEGs/PhD_datasets/BIDS/FILEPATH_AND_FILENAME.csv', index=False)\n",
    "print('CSV CREATED')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CSA PLOTS TO COMPARE WITH DF ESTIMATIONS - WELCH - HAMMING WINDOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.signal as signal\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib.collections import LineCollection\n",
    "from matplotlib.colors import ListedColormap, BoundaryNorm\n",
    "\n",
    "#raw = mne.read_epochs(\"D:/EEGs/PhD_datasets/BIDS/california/derivatives/sovaharmony/sub-pd28/ses-on/eeg/sub-pd28_ses-on_task-rest_desc-norm_eeg.fif\", preload=True)\n",
    "srate = raw.info['sfreq'] #This gets the sampling rate of the EEGLAB object\n",
    "nfft, noverlap, nperseg = int(2.5*srate), int(1.25*srate), int(2.5*srate)\n",
    "f, periodograma = signal.welch(raw._data[:,:,:], fs=srate, nfft=n_fft, noverlap=n_overlap, nperseg=n_per_seg, window='hamming', scaling='spectrum') #This calculates PSD Welch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "#raw = mne.read_epochs(\"D:/EEGs/PhD_datasets/BIDS/california/derivatives/sovaharmony/sub-pd28/ses-on/eeg/sub-pd28_ses-on_task-rest_desc-norm_eeg.fif\", preload=True)\n",
    "srate = raw.info['sfreq'] #This gets the sampling rate of the EEGLAB object\n",
    "n_fft, n_overlap, n_per_seg = int(2.5*srate), int(1.25*srate), int(2.5*srate)\n",
    "periodograma, f = mne.time_frequency.psd_array_multitaper(raw._data[:,:,:], sfreq = srate, fmin=4, bandwidth= 1, fmax=15, adaptive=True, normalization='full', verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>center</th>\n",
       "      <th>subject</th>\n",
       "      <th>channel</th>\n",
       "      <th>epoch</th>\n",
       "      <th>df_ep</th>\n",
       "      <th>dfv_ch</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>1</td>\n",
       "      <td>12.8</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>3.6</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>3</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.8</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>4</td>\n",
       "      <td>14.4</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>5</td>\n",
       "      <td>13.6</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.6</td>\n",
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       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>7</td>\n",
       "      <td>8.8</td>\n",
       "      <td>1.2</td>\n",
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       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>8</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>9</td>\n",
       "      <td>13.6</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>10</td>\n",
       "      <td>9.6</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>11</td>\n",
       "      <td>11.6</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>12</td>\n",
       "      <td>10.4</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>13</td>\n",
       "      <td>13.2</td>\n",
       "      <td>2.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>14</td>\n",
       "      <td>14.4</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>15</td>\n",
       "      <td>11.2</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>16</td>\n",
       "      <td>10.8</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>17</td>\n",
       "      <td>9.2</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>18</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>19</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>20</td>\n",
       "      <td>10.8</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>21</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>22</td>\n",
       "      <td>14.4</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>23</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>24</td>\n",
       "      <td>13.2</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>25</td>\n",
       "      <td>12.8</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>26</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>27</td>\n",
       "      <td>13.2</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>28</td>\n",
       "      <td>12.8</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>29</td>\n",
       "      <td>11.6</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>30</td>\n",
       "      <td>14.8</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>31</td>\n",
       "      <td>11.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>32</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>33</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>34</td>\n",
       "      <td>11.6</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>35</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>36</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>37</td>\n",
       "      <td>12.4</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>38</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>39</td>\n",
       "      <td>8.8</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>40</td>\n",
       "      <td>8.4</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>41</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>42</td>\n",
       "      <td>11.2</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>43</td>\n",
       "      <td>13.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>44</td>\n",
       "      <td>6.8</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>45</td>\n",
       "      <td>9.2</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>46</td>\n",
       "      <td>10.4</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>47</td>\n",
       "      <td>12.4</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>48</td>\n",
       "      <td>14.8</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>49</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>50</td>\n",
       "      <td>5.2</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>51</td>\n",
       "      <td>10.8</td>\n",
       "      <td>5.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>52</td>\n",
       "      <td>11.6</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>53</td>\n",
       "      <td>13.6</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>54</td>\n",
       "      <td>13.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>55</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>56</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>C5</td>\n",
       "      <td>57</td>\n",
       "      <td>13.6</td>\n",
       "      <td>9.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>FC4</td>\n",
       "      <td>0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>medellin</td>\n",
       "      <td>071</td>\n",
       "      <td>FC4</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      center subject channel  epoch  df_ep  dfv_ch\n",
       "0   medellin     071      C5      0    9.2     0.0\n",
       "1   medellin     071      C5      1   12.8     3.6\n",
       "2   medellin     071      C5      2    9.2     3.6\n",
       "3   medellin     071      C5      3   12.0     2.8\n",
       "4   medellin     071      C5      4   14.4     2.4\n",
       "5   medellin     071      C5      5   13.6     0.8\n",
       "6   medellin     071      C5      6   10.0     3.6\n",
       "7   medellin     071      C5      7    8.8     1.2\n",
       "8   medellin     071      C5      8    7.6     1.2\n",
       "9   medellin     071      C5      9   13.6     6.0\n",
       "10  medellin     071      C5     10    9.6     4.0\n",
       "11  medellin     071      C5     11   11.6     2.0\n",
       "12  medellin     071      C5     12   10.4     1.2\n",
       "13  medellin     071      C5     13   13.2     2.8\n",
       "14  medellin     071      C5     14   14.4     1.2\n",
       "15  medellin     071      C5     15   11.2     3.2\n",
       "16  medellin     071      C5     16   10.8     0.4\n",
       "17  medellin     071      C5     17    9.2     1.6\n",
       "18  medellin     071      C5     18    7.2     2.0\n",
       "19  medellin     071      C5     19    6.0     1.2\n",
       "20  medellin     071      C5     20   10.8     4.8\n",
       "21  medellin     071      C5     21   12.0     1.2\n",
       "22  medellin     071      C5     22   14.4     2.4\n",
       "23  medellin     071      C5     23   14.0     0.4\n",
       "24  medellin     071      C5     24   13.2     0.8\n",
       "25  medellin     071      C5     25   12.8     0.4\n",
       "26  medellin     071      C5     26   14.0     1.2\n",
       "27  medellin     071      C5     27   13.2     0.8\n",
       "28  medellin     071      C5     28   12.8     0.4\n",
       "29  medellin     071      C5     29   11.6     1.2\n",
       "30  medellin     071      C5     30   14.8     3.2\n",
       "31  medellin     071      C5     31   11.6     3.2\n",
       "32  medellin     071      C5     32   11.6     0.0\n",
       "33  medellin     071      C5     33   10.0     1.6\n",
       "34  medellin     071      C5     34   11.6     1.6\n",
       "35  medellin     071      C5     35   11.6     0.0\n",
       "36  medellin     071      C5     36   11.6     0.0\n",
       "37  medellin     071      C5     37   12.4     0.8\n",
       "38  medellin     071      C5     38   12.0     0.4\n",
       "39  medellin     071      C5     39    8.8     3.2\n",
       "40  medellin     071      C5     40    8.4     0.4\n",
       "41  medellin     071      C5     41    6.0     2.4\n",
       "42  medellin     071      C5     42   11.2     5.2\n",
       "43  medellin     071      C5     43   13.2     2.0\n",
       "44  medellin     071      C5     44    6.8     6.4\n",
       "45  medellin     071      C5     45    9.2     2.4\n",
       "46  medellin     071      C5     46   10.4     1.2\n",
       "47  medellin     071      C5     47   12.4     2.0\n",
       "48  medellin     071      C5     48   14.8     2.4\n",
       "49  medellin     071      C5     49   14.0     0.8\n",
       "50  medellin     071      C5     50    5.2     8.8\n",
       "51  medellin     071      C5     51   10.8     5.6\n",
       "52  medellin     071      C5     52   11.6     0.8\n",
       "53  medellin     071      C5     53   13.6     2.0\n",
       "54  medellin     071      C5     54   13.6     0.0\n",
       "55  medellin     071      C5     55   10.0     3.6\n",
       "56  medellin     071      C5     56    4.0     6.0\n",
       "57  medellin     071      C5     57   13.6     9.6\n",
       "58  medellin     071     FC4      0    6.8     0.0\n",
       "59  medellin     071     FC4      1    6.0     0.8"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw.ch_names.index('C5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "channel_plot = 1 ### REPLACE HERE THE CHANNEL INDEX YOU WANNA PLOT ###\n",
    "channel_name = raw.ch_names[channel_plot]\n",
    "\n",
    "x = f\n",
    "Nx = x.shape[0]\n",
    "\n",
    "pot_tot = np.sum(periodograma,axis=-1)\n",
    "\n",
    "num_epocas = periodograma.shape[0]\n",
    "Nz = num_epocas\n",
    "y1 = periodograma[:,channel_plot,:].T \n",
    "\n",
    "y = (y1/pot_tot[:,channel_plot])*100 \n",
    "\n",
    "\n",
    "# create waterfall plot\n",
    "fig = plt.figure(figsize=(10,15))\n",
    "ax = fig.add_subplot(111)\n",
    "for side in ['right', 'top', 'left']:\n",
    "    ax.spines[side].set_visible(False)\n",
    "    \n",
    "# some usefull parameters\n",
    "highest = np.max(y)\n",
    "lowest = np.min(y)\n",
    "delta = highest-lowest\n",
    "t = np.sqrt(abs(delta))/4 # a tuning parameter for the offset of each dataset\n",
    "\n",
    "for i in np.flip(range(Nz)):\n",
    "    yi_ = y[:,i]       # the y data set\n",
    "    yi = yi_ + i*t   # the shifted y data set used for plotting\n",
    "    zindex = Nz-i # used to set zorder\n",
    "\n",
    "    # fill with white from the (shifted) y data down to the lowest value\n",
    "    # for good results, don't make the alpha (TRANSPARENCY) too low, otherwise you'll get confusing blending of lines\n",
    "    ax.fill_between(x, lowest, yi, facecolor=\"white\", alpha=0.8, zorder=zindex)\n",
    "\n",
    "    # cut the data into segments that can be colored individually\n",
    "    points = np.array([x, yi]).T.reshape(-1, 1, 2)\n",
    "    segments = np.concatenate([points[:-1], points[1:]], axis=1)\n",
    "\n",
    "    # Create a continuous norm to map from data points to colors\n",
    "    norm = plt.Normalize(lowest, highest)\n",
    "    lc = LineCollection(segments, cmap='plasma', norm=norm)\n",
    "    \n",
    "    # Set the values used for colormapping\n",
    "    lc.set_array(yi_)\n",
    "    lc.set_zorder(zindex)\n",
    "    lc.set_linewidth(1)\n",
    "    line = ax.add_collection(lc)\n",
    "    \n",
    "    # print text indicating epochs number\n",
    "    delta_x = max(x)-min(x)\n",
    "    if (i)%1==0:\n",
    "        ax.text(min(x)-0.5, t*i, \"%i\"%i)\n",
    "\n",
    "\n",
    "# set limits, as using LineCollection does not automatically set these\n",
    "ax.set_xlim(3.8, 15.2)\n",
    "\n",
    "psd_colorbar = fig.colorbar(line, ax=ax)\n",
    "psd_colorbar.set_label( label = 'Relative PSD (%)', size = 18, weight='bold')\n",
    "\n",
    "\n",
    "ax.set_title('Compressed Spectral Array ' + str(channel_name), size=22, fontweight=\"bold\")\n",
    "plt.yticks([])\n",
    "ax.set_ylabel('Epoch', size=18, fontweight=\"bold\")\n",
    "ax.set_xlabel('Frequency', size=18, fontweight=\"bold\")\n",
    "ax.yaxis.set_label_coords(-0.05, 0.5)\n",
    "ax.yaxis.set_ticks_position('none')"
   ]
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