-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathburst_calc.py
412 lines (296 loc) · 10.1 KB
/
burst_calc.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
from os import stat
import numpy as np
from scipy import stats
import mne
import numpy
def Time_Frequency_Estimation(signal):
freqs = np.arange(1, 101)
power = mne.decoding.TimeFrequency(
freqs, sfreq=1600, method="morlet", n_cycles=10, output="power"
)
run_TF = power.transform(signal)
return run_TF
def beta_bands(run_TF):
"""
Beta bands of the ecog channels: low beta(13-20Hz), high beta (20-35Hz), full beta (13-35Hz)
"""
THETA = (4, 9)
MU = (8, 13)
LOW_BETA = (13, 21)
HIGH_BETA = (20, 36)
FULL_BETA = (13, 36)
l_theta = []
l_mu = []
l_low_beta = []
l_high_beta = []
l_full_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, THETA[0] : THETA[1], :])
l_mu.append(run_TF[ch_idx, MU[0] : MU[1], :])
l_low_beta.append(run_TF[ch_idx, LOW_BETA[0] : LOW_BETA[1], :])
l_high_beta.append(run_TF[ch_idx, HIGH_BETA[0] : HIGH_BETA[1], :])
l_full_beta.append(run_TF[ch_idx, FULL_BETA[0] : FULL_BETA[1], :])
return l_theta, l_mu, l_low_beta, l_high_beta, l_full_beta
def beta_bands_sub3(run_TF):
indTHETA = (5, 8)
indBETA = (19, 22)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def beta_bands_sub3_on(run_TF):
indTHETA = (4, 7)
indBETA = (11, 14)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def beta_bands_sub4(run_TF):
indTHETA = (4, 7)
indBETA = (13, 16)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def beta_bands_sub4_on(run_TF):
indTHETA = (4, 7)
indBETA = (25, 28)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def beta_bands_sub5(run_TF):
indTHETA = (5, 8)
indBETA = (14, 17)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def beta_bands_sub5_on(run_TF):
indTHETA = (4, 7)
indBETA = (14, 17)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def beta_bands_sub6(run_TF):
indTHETA = (4, 7)
indBETA = (24, 27)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def beta_bands_sub7(run_TF):
indTHETA = (4, 7)
indBETA = (20, 23)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def beta_bands_sub7_on(run_TF):
indTHETA = (5, 8)
indBETA = (20, 23)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def beta_bands_sub8(run_TF):
indTHETA = (4, 7)
indBETA = (15, 18)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def beta_bands_sub8_on(run_TF):
indTHETA = (6, 9)
indBETA = (12, 15)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def beta_bands_sub9(run_TF):
indTHETA = (9, 12)
indBETA = (18, 21)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def beta_bands_sub9_on(run_TF):
indTHETA = (8, 11)
indBETA = (16, 19)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def beta_bands_sub11(run_TF):
indTHETA = (7, 10)
indBETA = (16, 19)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def beta_bands_sub11_on(run_TF):
indTHETA = (7, 10)
indBETA = (17, 20)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def beta_bands_sub12(run_TF):
indTHETA = (9, 12)
indBETA = (22, 25)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def beta_bands_sub12_on(run_TF):
indTHETA = (6, 9)
indBETA = (23, 26)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def beta_bands_sub13(run_TF):
indTHETA = (7, 10)
indBETA = (22, 25)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def beta_bands_sub13_on(run_TF):
indTHETA = (7, 10)
indBETA = (23, 26)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def beta_bands_sub14(run_TF):
indTHETA = (4, 7)
indBETA = (19, 22)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def beta_bands_sub14_on(run_TF):
indTHETA = (8, 11)
indBETA = (19, 22)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def beta_bands_sub15(run_TF):
indTHETA = (4, 7)
indBETA = (16, 19)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def beta_bands_sub15_on(run_TF):
indTHETA = (4, 7)
indBETA = (20, 23)
l_theta = []
l_beta = []
for ch_idx in range(run_TF.shape[0]):
l_theta.append(run_TF[ch_idx, indTHETA[0] : indTHETA[1], :])
l_beta.append(run_TF[ch_idx, indBETA[0] :indBETA[1], :])
return l_theta, l_beta
def avg_power(l_beta):
return [np.mean(l_ch, axis=0) for l_ch in l_beta]
def z_score(l_beta):
return [stats.zscore(l_ch, axis=0) for l_ch in l_beta]
def percentile(l_beta, percentile):
return [np.percentile(l_beta, q=percentile)] # for l_ch in l_beta]
def get_burst_length(beta_averp_norm, beta_thr, sfreq):
"""
Analysing the duration of beta burst
"""
bursts = np.zeros((beta_averp_norm.shape[0] + 1), dtype=bool)
bursts[1:] = beta_averp_norm >= beta_thr
deriv = np.diff(bursts)
isburst = False
burst_length = []
burst_start = 0
for index, i in enumerate(deriv):
if i == True:
if isburst == True:
burst_length.append(index - burst_start)
isburst = False
else:
burst_start = index
isburst = True
if isburst:
burst_length.append(index + 1 - burst_start)
burst_length = np.array(burst_length) / sfreq
return burst_length
def exclude_short_bursts(burst_length):
"""
exclude bursts shorter than 100ms
"""
return [i for i in burst_length if i >= 0.1]
def get_burst_amplitude(beta_amplitude, beta_thr):
amplitude = []
no_bursts = True
cont = False
for val in beta_amplitude:
if val >= beta_thr:
no_bursts = False
if cont == False:
burst = [val]
cont = True
else:
burst.append(val)
else:
if not no_bursts:
cont = False
amplitude.append(burst)
mean_amplitude_single_burst = [np.mean(burst) for burst in amplitude]
mean_amplitude = np.mean(mean_amplitude_single_burst)
return mean_amplitude
def smooth(array, window_size=320):
kernel = np.ones(window_size) / window_size
data_convolved = np.convolve(array, kernel, mode='same')
return data_convolved