forked from fieldtrip/fieldtrip
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathft_freqanalysis.m
1114 lines (1009 loc) · 49.7 KB
/
ft_freqanalysis.m
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
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
function [freq] = ft_freqanalysis(cfg, data)
% FT_FREQANALYSIS performs frequency and time-frequency analysis
% on time series data over multiple trials
%
% Use as
% [freq] = ft_freqanalysis(cfg, data)
%
% The input data should be organised in a structure as obtained from
% the FT_PREPROCESSING or the FT_MVARANALYSIS function. The configuration
% depends on the type of computation that you want to perform.
%
% The configuration should contain:
% cfg.method = different methods of calculating the spectra
% 'mtmfft', analyses an entire spectrum for the entire data
% length, implements multitaper frequency transformation.
% 'mtmconvol', implements multitaper time-frequency
% transformation based on multiplication in the
% frequency domain.
% 'wavelet', implements wavelet time frequency
% transformation (using Morlet wavelets) based on
% multiplication in the frequency domain.
% 'tfr', implements wavelet time frequency
% transformation (using Morlet wavelets) based on
% convolution in the time domain.
% 'mvar', does a fourier transform on the coefficients
% of an estimated multivariate autoregressive model,
% obtained with FT_MVARANALYSIS. In this case, the
% output will contain a spectral transfer matrix,
% the cross-spectral density matrix, and the
% covariance matrix of the innovatio noise.
% 'superlet', combines Morlet-wavelet based
% decompositions, see below.
% 'irasa', implements Irregular-Resampling Auto-Spectral
% Analysis (IRASA), to separate the fractal components
% from the periodicities in the signal.
% cfg.output = 'pow' return the power-spectra
% 'powandcsd' return the power and the cross-spectra
% 'fourier' return the complex Fourier-spectra
% 'fractal' (when cfg.method = 'irasa'), return the
% fractal component of the spectrum (1/f)
% 'original' (when cfg.method = 'irasa'), return the
% full power spectrum
% 'fooof' returns a smooth power-spectrum,
% based on a parametrization of a mixture of aperiodic and periodic
% components (only works with cfg.method = 'mtmfft')
% 'fooof_aperiodic' returns a power-spectrum with the
% fooof based estimate of the aperiodic part of the signal.
% 'fooof_peaks' returns a power-spectrum with the fooof
% based estimate of the aperiodic signal removed,
% it's expressed as
% 10^(log10(fooof)-log10(fooof_aperiodic))
% cfg.channel = Nx1 cell-array with selection of channels (default = 'all'),
% see FT_CHANNELSELECTION for details
% cfg.channelcmb = Mx2 cell-array with selection of channel pairs (default = {'all' 'all'}),
% see FT_CHANNELCOMBINATION for details
% cfg.trials = 'all' or a selection given as a 1xN vector (default = 'all')
% cfg.keeptrials = 'yes' or 'no', return individual trials or average (default = 'no')
% cfg.keeptapers = 'yes' or 'no', return individual tapers or average (default = 'no')
% cfg.pad = number, 'nextpow2', or 'maxperlen' (default), length
% in seconds to which the data can be padded out. The
% padding will determine your spectral resolution. If you
% want to compare spectra from data pieces of different
% lengths, you should use the same cfg.pad for both, in
% order to spectrally interpolate them to the same
% spectral resolution. The new option 'nextpow2' rounds
% the maximum trial length up to the next power of 2. By
% using that amount of padding, the FFT can be computed
% more efficiently in case 'maxperlen' has a large prime
% factor sum.
% cfg.padtype = string, type of padding (default 'zero', see
% ft_preproc_padding)
% cfg.polyremoval = number (default = 0), specifying the order of the
% polynome which is fitted and subtracted from the time
% domain data prior to the spectral analysis. For
% example, a value of 1 corresponds to a linear trend.
% The default is a mean subtraction, thus a value of 0.
% If no removal is requested, specify -1.
% see FT_PREPROC_POLYREMOVAL for details
%
%
% METHOD SPECIFIC OPTIONS AND DESCRIPTIONS
%
% MTMFFT performs frequency analysis on any time series trial data using a
% conventional single taper (e.g. Hanning) or using the multiple tapers based on
% discrete prolate spheroidal sequences (DPSS), also known as the Slepian
% sequence.
% cfg.taper = 'dpss', 'hanning' or many others, see WINDOW (default = 'dpss')
% For cfg.output='powandcsd', you should specify the channel combinations
% between which to compute the cross-spectra as cfg.channelcmb. Otherwise
% you should specify only the channels in cfg.channel.
% cfg.foilim = [begin end], frequency band of interest
% OR
% cfg.foi = vector 1 x numfoi, frequencies of interest
% cfg.tapsmofrq = number, the amount of spectral smoothing through
% multi-tapering. Note that 4 Hz smoothing means
% plus-minus 4 Hz, i.e. a 8 Hz smoothing box.
%
% MTMCONVOL performs time-frequency analysis on any time series trial data using
% the 'multitaper method' (MTM) based on Slepian sequences as tapers.
% Alternatively, you can use conventional tapers (e.g. Hanning).
% cfg.tapsmofrq = vector 1 x numfoi, the amount of spectral smoothing
% through multi-tapering. Note that 4 Hz smoothing means
% plus-minus 4 Hz, i.e. a 8 Hz smoothing box.
% cfg.foi = vector 1 x numfoi, frequencies of interest
% cfg.taper = 'dpss', 'hanning' or many others, see WINDOW (default = 'dpss')
% For cfg.output='powandcsd', you should specify the channel combinations
% between which to compute the cross-spectra as cfg.channelcmb. Otherwise
% you should specify only the channels in cfg.channel.
% cfg.t_ftimwin = vector 1 x numfoi, length of time window (in seconds)
% cfg.toi = vector 1 x numtoi, the times on which the analysis
% windows should be centered (in seconds), or a string
% such as '50%' or 'all' (default). Both string options
% use all timepoints available in the data, but 'all'
% centers a spectral estimate on each sample, whereas
% the percentage specifies the degree of overlap between
% the shortest time windows from cfg.t_ftimwin.
%
% WAVELET performs time-frequency analysis on any time series trial data using the
% 'wavelet method' based on Morlet wavelets. Using mulitplication in the frequency
% domain instead of convolution in the time domain.
% cfg.foi = vector 1 x numfoi, frequencies of interest
% OR
% cfg.foilim = [begin end], frequency band of interest
% cfg.toi = vector 1 x numtoi, the times on which the analysis
% windows should be centered (in seconds)
% cfg.width = 'width', or number of cycles, of the wavelet (default = 7)
% cfg.gwidth = determines the length of the used wavelets in standard
% deviations of the implicit Gaussian kernel and should
% be chosen >= 3; (default = 3)
%
% The standard deviation in the frequency domain (sf) at frequency f0 is
% defined as: sf = f0/width
% The standard deviation in the temporal domain (st) at frequency f0 is
% defined as: st = 1/(2*pi*sf)
%
% SUPERLET performs time-frequency analysis on any time series trial data using the
% 'superlet method' based on a frequency-wise combination of Morlet wavelets of varying cycle
% widths (see Moca et al. 2019, https://doi.org/10.1101/583732).
% cfg.foi = vector 1 x numfoi, frequencies of interest
% OR
% cfg.foilim = [begin end], frequency band of interest
% cfg.toi = vector 1 x numtoi, the times on which the analysis
% windows should be centered (in seconds)
% cfg.width = 'width', or number of cycles, of the base wavelet (default = 3)
% cfg.gwidth = determines the length of the used wavelets in standard
% deviations of the implicit Gaussian kernel and should
% be chosen >= 3; (default = 3)
% cfg.combine = 'additive', 'multiplicative' (default = 'additive')
% determines if cycle numbers of wavelets comprising a superlet
% are chosen additively or multiplicatively
% cfg.order = vector 1 x numfoi, superlet order, i.e. number of combined
% wavelets, for individual frequencies of interest.
%
% The standard deviation in the frequency domain (sf) at frequency f0 is
% defined as: sf = f0/width
% The standard deviation in the temporal domain (st) at frequency f0 is
% defined as: st = 1/(2*pi*sf)
%
% HILBERT performs time-frequency analysis on any time series data using a frequency specific
% bandpass filter, followed by the Hilbert transform.
% cfg.foi = vector 1 x numfoi, frequencies of interest
% cfg.toi = vector 1 x numtoi, the time points for which the estimates will be returned (in seconds)
% cfg.width = scalar, or vector (default: 1), specifying the half bandwidth of the filter;
% cfg.edgartnan = 'no' (default) or 'yes', replace filter edges with nans, works only for finite impulse response (FIR) filters, and
% requires a user specification of the filter order
%
% For the bandpass filtering the following options can be specified, the default values are as in FT_PREPROC_BANDPASSFILTER, for more
% information see the help of FT_PREPROCESSING
% cfg.bpfilttype
% cfg.bpfiltord = (optional) scalar, or vector 1 x numfoi;
% cfg.bpfiltdir
% cfg.bpinstabilityfix
% cfg.bpfiltdf
% cfg.bpfiltwintype
% cfg.bpfiltdev
%
% TFR performs time-frequency analysis on any time series trial data using the
% 'wavelet method' based on Morlet wavelets. Using convolution in the time domain
% instead of multiplication in the frequency domain.
% cfg.foi = vector 1 x numfoi, frequencies of interest
% OR
% cfg.foilim = [begin end], frequency band of interest
% cfg.width = 'width', or number of cycles, of the wavelet (default = 7)
% cfg.gwidth = determines the length of the used wavelets in standard
% deviations of the implicit Gaussian kernel and should
% be choosen >= 3; (default = 3)
%
%
% To facilitate data-handling and distributed computing you can use
% cfg.inputfile = ...
% cfg.outputfile = ...
% If you specify one of these (or both) the input data will be read from a
% *.mat file on disk and/or the output data will be written to a *.mat
% file. These mat files should contain only a single variable,
% corresponding with the input/output structure.
%
% See also FT_FREQSTATISTICS, FT_FREQDESCRIPTIVES, FT_CONNECTIVITYANALYSIS
% Guidelines for use in an analysis pipeline:
% after FT_FREQANALYSIS you will have frequency or time-frequency
% representations (TFRs) of the data, represented as power-spectra,
% power and cross-spectra, or complex fourier-spectra, either for individual
% trials or an average over trials.
% This usually serves as input for one of the following functions:
% * FT_FREQDESCRIPTIVES to compute descriptive univariate statistics
% * FT_FREQSTATISTICS to perform parametric or non-parametric statistical tests
% * FT_FREQBASELINE to perform baseline normalization of the spectra
% * FT_FREQGRANDAVERAGE to compute the average spectra over multiple subjects or datasets
% * FT_CONNECTIVITYANALYSIS to compute various measures of connectivity
% Furthermore, the data can be visualised using the various plotting
% functions, including:
% * FT_SINGLEPLOTTFR to plot the TFR of a single channel or the average over multiple channels
% * FT_TOPOPLOTTFR to plot the topographic distribution over the head
% * FT_MULTIPLOTTFR to plot TFRs in a topographical layout
% Undocumented local options:
% cfg.method = 'hilbert'. Keeping this as undocumented as it does not make
% sense to use in ft_freqanalysis unless the user is doing his
% own filter-padding to remove edge-artifacts
% cfg.correctt_ftimwin (set to yes to try to determine new t_ftimwins based
% on correct cfg.foi)
% Copyright (C) 2003-2006, F.C. Donders Centre, Pascal Fries
% Copyright (C) 2004-2006, F.C. Donders Centre, Markus Siegel
% Copyright (C) 2007-2012, DCCN, The FieldTrip team
%
% This file is part of FieldTrip, see http://www.fieldtriptoolbox.org
% for the documentation and details.
%
% FieldTrip is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% FieldTrip is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with FieldTrip. If not, see <http://www.gnu.org/licenses/>.
% these are used by the ft_preamble/ft_postamble function and scripts
ft_revision = '$Id$';
ft_nargin = nargin;
ft_nargout = nargout;
% do the general setup of the function
ft_defaults
ft_preamble init
ft_preamble debug
ft_preamble loadvar data
ft_preamble provenance data
ft_preamble trackconfig
% the ft_abort variable is set to true or false in ft_preamble_init
if ft_abort
return
end
% check if the input data is valid for this function
data = ft_checkdata(data, 'datatype', {'raw', 'raw+comp', 'mvar'}, 'feedback', 'yes', 'hassampleinfo', 'yes');
% check if the input cfg is valid for this function
cfg = ft_checkconfig(cfg, 'forbidden', {'channels', 'trial'}); % prevent accidental typos, see issue 1729
cfg = ft_checkconfig(cfg, 'renamed', {'label', 'channel'});
cfg = ft_checkconfig(cfg, 'renamed', {'sgn', 'channel'});
cfg = ft_checkconfig(cfg, 'renamed', {'labelcmb', 'channelcmb'});
cfg = ft_checkconfig(cfg, 'renamed', {'sgncmb', 'channelcmb'});
cfg = ft_checkconfig(cfg, 'required', {'method'});
cfg = ft_checkconfig(cfg, 'renamedval', {'method', 'fft', 'mtmfft'});
cfg = ft_checkconfig(cfg, 'renamedval', {'method', 'convol', 'mtmconvol'});
cfg = ft_checkconfig(cfg, 'forbidden', {'latency'}); % see bug 1376 and 1076
cfg = ft_checkconfig(cfg, 'renamedval', {'method', 'wltconvol', 'wavelet'});
% set the defaults
cfg.feedback = ft_getopt(cfg, 'feedback', 'text');
cfg.inputlock = ft_getopt(cfg, 'inputlock', []); % this can be used as mutex when doing distributed computation
cfg.outputlock = ft_getopt(cfg, 'outputlock', []); % this can be used as mutex when doing distributed computation
cfg.trials = ft_getopt(cfg, 'trials', 'all', 1);
cfg.channel = ft_getopt(cfg, 'channel', 'all');
% select channels and trials of interest, by default this will select all channels and trials
tmpcfg = keepfields(cfg, {'trials', 'channel', 'tolerance', 'showcallinfo', 'trackcallinfo', 'trackconfig', 'trackusage', 'trackdatainfo', 'trackmeminfo', 'tracktimeinfo'});
data = ft_selectdata(tmpcfg, data);
% restore the provenance information
[cfg, data] = rollback_provenance(cfg, data);
% some proper error handling
if isfield(data, 'trial') && numel(data.trial)==0
ft_error('no trials were selected'); % this does not apply for MVAR data
end
if numel(data.label)==0
ft_error('no channels were selected');
end
% switch over method and do some of the method specfic checks and defaulting
switch cfg.method
case 'mtmconvol'
cfg.taper = ft_getopt(cfg, 'taper', 'dpss');
if isequal(cfg.taper, 'dpss') && ~isfield(cfg, 'tapsmofrq')
ft_error('you must specify a smoothing parameter with taper = dpss');
end
% check for foi above Nyquist
if isfield(cfg, 'foi')
if any(cfg.foi > (data.fsample+100*eps(data.fsample))/2)
% add a small number to allow for numeric tolerance issues
ft_error('frequencies in cfg.foi are above Nyquist')
end
if isequal(cfg.taper, 'dpss') && not(isfield(cfg, 'tapsmofrq'))
ft_error('you must specify a smoothing parameter with taper = dpss');
end
end
cfg = ft_checkconfig(cfg, 'required', {'toi', 't_ftimwin'});
if ischar(cfg.toi)
begtim = min(cellfun(@min,data.time));
endtim = max(cellfun(@max,data.time));
if strcmp(cfg.toi, 'all') % each data sample gets a time window
cfg.toi = linspace(begtim, endtim, round((endtim-begtim) ./ ...
mean(diff(data.time{1})))+1);
elseif strcmp(cfg.toi(end), '%') % percent overlap between smallest time windows
overlap = str2double(cfg.toi(1:(end-1)))/100;
cfg.toi = linspace(begtim, endtim, round((endtim-begtim) ./ ...
(overlap * min(cfg.t_ftimwin))) + 1);
else
ft_error('cfg.toi should be either a numeric vector or a string: can be ''all'' or a percentage (e.g., ''50%'')');
end
end
case 'mtmfft'
cfg.taper = ft_getopt(cfg, 'taper', 'dpss');
if isequal(cfg.taper, 'dpss') && not(isfield(cfg, 'tapsmofrq'))
ft_error('you must specify a smoothing parameter with taper = dpss');
end
% check for foi above Nyquist
if isfield(cfg, 'foi')
if any(cfg.foi > (data.fsample/2))
ft_error('frequencies in cfg.foi are above Nyquist')
end
end
if isequal(cfg.taper, 'dpss') && not(isfield(cfg, 'tapsmofrq'))
ft_error('you must specify a smoothing parameter with taper = dpss');
end
case 'irasa'
cfg.taper = ft_getopt(cfg, 'taper', 'hanning');
cfg.output = ft_getopt(cfg, 'output', 'fractal');
cfg.pad = ft_getopt(cfg, 'pad', 'nextpow2');
if ~isequal(cfg.taper, 'hanning')
ft_error('the irasa method supports hanning tapers only');
end
if ~isequal(cfg.pad, 'nextpow2')
ft_warning('consider using cfg.pad=''nextpow2'' for the irasa method');
end
% check for foi above Nyquist
if isfield(cfg, 'foi')
if any(cfg.foi > (data.fsample/2))
ft_error('frequencies in cfg.foi are above Nyquist')
end
end
case 'wavelet'
cfg.width = ft_getopt(cfg, 'width', 7);
cfg.gwidth = ft_getopt(cfg, 'gwidth', 3);
case 'superlet'
% reorganize the cfg, a nested cfg is not consistent with the othe methods
cfg = ft_checkconfig(cfg, 'createtopcfg', 'superlet');
cfg = removefields(cfg, 'superlet');
cfg = ft_checkconfig(cfg, 'renamed', {'basewidth', 'width'});
cfg.width = ft_getopt(cfg, 'width', 3);
cfg.gwidth = ft_getopt(cfg, 'gwidth', 3);
cfg.combine = ft_getopt(cfg, 'combine', 'additive');
cfg.order = ft_getopt(cfg, 'order', ones(1, numel(cfg.foi)));
if numel(cfg.order) == 1
cfg.order = cfg.order.*length(cfg.foi);
elseif numel(cfg.order)~= numel(cfg.foi)
ft_error('cfg.foi must have the same number of elements as cfg.foi, or must be a scalar');
end
case 'tfr'
cfg = ft_checkconfig(cfg, 'renamed', {'waveletwidth', 'width'});
cfg = ft_checkconfig(cfg, 'unused', {'downsample'});
cfg.width = ft_getopt(cfg, 'width', 7);
cfg.gwidth = ft_getopt(cfg, 'gwidth', 3);
case 'hilbert'
ft_warning('method = hilbert may require user action to deal with filtering-artifacts')
cfg = ft_checkconfig(cfg, 'renamed', {'filttype', 'bpfilttype'});
cfg = ft_checkconfig(cfg, 'renamed', {'filtorder', 'bpfiltord'});
cfg = ft_checkconfig(cfg, 'renamed', {'filtdir', 'bpfiltdir'});
cfg.bpfilttype = ft_getopt(cfg, 'bpfilttype');
cfg.bpfiltord = ft_getopt(cfg, 'bpfiltord');
cfg.bpfiltdir = ft_getopt(cfg, 'bpfiltdir');
cfg.bpinstabilityfix = ft_getopt(cfg, 'bpinstabilityfix');
cfg.bpfiltdf = ft_getopt(cfg, 'bpfiltdf');
cfg.bpfiltwintype = ft_getopt(cfg, 'bpfiltwintype');
cfg.bpfiltdev = ft_getopt(cfg, 'bpfiltdev');
cfg.width = ft_getopt(cfg, 'width', 1);
cfg.edgartnan = istrue(ft_getopt(cfg, 'edgeartnan', 'no'));
fn = fieldnames(cfg);
bpfiltoptions = ft_cfg2keyval(keepfields(cfg, fn(startsWith(fn, 'bp'))));
case 'mvar'
if isfield(cfg, 'inputfile')
freq = ft_freqanalysis_mvar(cfg);
else
freq = ft_freqanalysis_mvar(cfg, data);
end
return
case 'neuvar'
cfg.order = ft_getopt(cfg, 'order', 1); % order of differentiation
otherwise
ft_error('specified cfg.method is not supported')
end
% set all the defaults
cfg.pad = ft_getopt(cfg, 'pad', []);
if isempty(cfg.pad)
ft_notice('Default cfg.pad=''maxperlen'' can run slowly. Consider using cfg.pad=''nextpow2'' for more efficient FFT computation.')
cfg.pad = 'maxperlen';
end
cfg.padtype = ft_getopt(cfg, 'padtype', 'zero');
cfg.output = ft_getopt(cfg, 'output', 'pow'); % the default for irasa is set earlier
cfg.calcdof = ft_getopt(cfg, 'calcdof', 'no');
cfg.channel = ft_getopt(cfg, 'channel', 'all');
cfg.precision = ft_getopt(cfg, 'precision', 'double');
cfg.foi = ft_getopt(cfg, 'foi', []);
cfg.foilim = ft_getopt(cfg, 'foilim', []);
cfg.correctt_ftimwin = ft_getopt(cfg, 'correctt_ftimwin', 'no');
cfg.polyremoval = ft_getopt(cfg, 'polyremoval', 0);
% keeptrials and keeptapers should be conditional on cfg.output,
% cfg.output = 'fourier' should always output tapers
if strcmp(cfg.output, 'fourier')
cfg.keeptrials = ft_getopt(cfg, 'keeptrials', 'yes');
cfg.keeptapers = ft_getopt(cfg, 'keeptapers', 'yes');
if strcmp(cfg.keeptrials, 'no') || strcmp(cfg.keeptapers, 'no')
ft_error('cfg.output = ''fourier'' requires cfg.keeptrials = ''yes'' and cfg.keeptapers = ''yes''');
end
else
cfg.keeptrials = ft_getopt(cfg, 'keeptrials', 'no');
cfg.keeptapers = ft_getopt(cfg, 'keeptapers', 'no');
end
% set flags for keeping trials and/or tapers
if strcmp(cfg.keeptrials, 'no') && strcmp(cfg.keeptapers, 'no')
keeprpt = 1;
elseif strcmp(cfg.keeptrials, 'yes') && strcmp(cfg.keeptapers, 'no')
keeprpt = 2;
elseif strcmp(cfg.keeptrials, 'no') && strcmp(cfg.keeptapers, 'yes')
ft_error('There is currently no support for keeping tapers WITHOUT KEEPING TRIALS.');
elseif strcmp(cfg.keeptrials, 'yes') && strcmp(cfg.keeptapers, 'yes')
keeprpt = 4;
end
if strcmp(cfg.keeptrials, 'yes') && strcmp(cfg.keeptapers, 'yes')
if ~strcmp(cfg.output, 'fourier')
ft_error('Keeping trials AND tapers is only possible with fourier as the output.');
end
end
% Set flags for output
if ismember(cfg.output, {'pow','fractal','original','fooof','fooof_peaks','fooof_aperiodic'})
powflg = 1;
csdflg = 0;
fftflg = 0;
elseif strcmp(cfg.output, 'powandcsd')
powflg = 1;
csdflg = 1;
fftflg = 0;
elseif strcmp(cfg.output, 'fourier')
powflg = 0;
csdflg = 0;
fftflg = 1;
else
ft_error('Unrecognized output required');
end
% Check whether the keeptrials is correct for fooof
if startsWith(cfg.output, 'fooof')
% ensure that Brainstorm is on the path: if the user uses their own
% version of the code, assume that the paths are correctly set
if keeprpt~=1
ft_error('Keeping trials and/or tapers is not allowed when using fooof');
end
if ~isequal(cfg.method, 'mtmfft')
ft_error('Fooof is only supported with cfg.method = ''mtmfft''');
end
end
% prepare channel(cmb)
if ~isfield(cfg, 'channelcmb') && csdflg
%set the default for the channelcombination
cfg.channelcmb = {'all' 'all'};
elseif isfield(cfg, 'channelcmb') && ~csdflg
% no cross-spectrum needs to be computed, hence remove the combinations from cfg
cfg = rmfield(cfg, 'channelcmb');
end
if isfield(cfg, 'channelcmb')
% the channels in the data are already the subset according to cfg.channel
cfg.channelcmb = ft_channelcombination(cfg.channelcmb, data.label);
end
% determine the corresponding indices of all channels
chanind = match_str(data.label, cfg.channel);
nchan = numel(chanind);
if csdflg
assert(nchan>1, 'CSD output requires multiple channels');
% determine the corresponding indices of all channel combinations
[dummy,chancmbind(:,1)] = match_str(cfg.channelcmb(:,1), data.label);
[dummy,chancmbind(:,2)] = match_str(cfg.channelcmb(:,2), data.label);
nchancmb = size(chancmbind,1);
chanind = unique([chanind(:); chancmbind(:)]);
nchan = length(chanind);
cutdatindcmb = zeros(size(chancmbind));
for ichan = 1:nchan
cutdatindcmb(chancmbind == chanind(ichan)) = ichan;
end
end
% determine trial characteristics
ntrials = numel(data.trial);
trllength = zeros(1, ntrials);
for itrial = 1:ntrials
trllength(itrial) = size(data.trial{itrial}, 2);
end
if strcmp(cfg.pad, 'maxperlen')
padding = max(trllength);
cfg.pad = padding/data.fsample;
elseif strcmp(cfg.pad, 'nextpow2')
padding = 2^nextpow2(max(trllength));
cfg.pad = padding/data.fsample;
else
padding = cfg.pad*data.fsample;
if padding<max(trllength)
ft_error('the specified padding is too short');
end
end
% correct foi and implement foilim 'backwards compatibility'
if ~isempty(cfg.foi) && ~isempty(cfg.foilim)
ft_error('use either cfg.foi or cfg.foilim')
elseif ~isempty(cfg.foilim)
% get the full foi in the current foilim and set it too be used as foilim
fboilim = round(cfg.foilim .* cfg.pad) + 1;
fboi = fboilim(1):1:fboilim(2);
cfg.foi = (fboi-1) ./ cfg.pad;
else
% correct foi if foilim was empty and try to correct t_ftimwin (by detecting whether there is a constant factor between foi and t_ftimwin: cyclenum)
oldfoi = cfg.foi;
fboi = round(cfg.foi .* cfg.pad) + 1;
cfg.foi = (fboi-1) ./ cfg.pad; % boi - 1 because 0 Hz is included in fourier output
if strcmp(cfg.correctt_ftimwin, 'yes')
cyclenum = oldfoi .* cfg.t_ftimwin;
cfg.t_ftimwin = cyclenum ./ cfg.foi;
end
end
% tapsmofrq compatibility between functions (make it into a vector if it's not)
if isfield(cfg, 'tapsmofrq')
if strcmp(cfg.method, 'mtmconvol') && length(cfg.tapsmofrq) == 1 && length(cfg.foi) ~= 1
cfg.tapsmofrq = ones(length(cfg.foi),1) * cfg.tapsmofrq;
elseif strcmp(cfg.method, 'mtmfft') && length(cfg.tapsmofrq) ~= 1
ft_warning('cfg.tapsmofrq should be a single number when cfg.method = mtmfft, now using only the first element')
cfg.tapsmofrq = cfg.tapsmofrq(1);
end
end
% options that don't change over trials
if isfield(cfg, 'tapsmofrq')
options = {'pad', cfg.pad, 'padtype', cfg.padtype, 'freqoi', cfg.foi, 'tapsmofrq', cfg.tapsmofrq, 'polyorder', cfg.polyremoval, 'output', cfg.output};
else
options = {'pad', cfg.pad, 'padtype', cfg.padtype, 'freqoi', cfg.foi, 'polyorder', cfg.polyremoval, 'output', cfg.output};
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Main loop over trials, inside fourierspectra are obtained and transformed into the appropriate outputs
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% this is done on trial basis to save memory
ft_progress('init', cfg.feedback, 'processing trials');
trlcnt = []; % only some methods need this variable, but it needs to be defined outside the trial loop
for itrial = 1:ntrials
fbopt.i = itrial;
fbopt.n = ntrials;
dat = data.trial{itrial}; % chansel has already been performed
time = data.time{itrial};
clear spectrum % in case of very large trials, this lowers peak mem usage a bit
% Perform specest call and set some specifics
switch cfg.method
case 'mtmconvol'
[spectrum_mtmconvol,ntaper,foi,toi] = ft_specest_mtmconvol(dat, time, 'timeoi', cfg.toi, 'timwin', cfg.t_ftimwin, 'taper', ...
cfg.taper, options{:}, 'dimord', 'chan_time_freqtap', 'feedback', fbopt);
% the following variable is created to keep track of the number of
% trials per time bin and is needed for proper normalization if
% keeprpt==1 and the triallength is variable
if itrial==1, trlcnt = zeros(1, numel(foi), numel(toi)); end
hastime = true;
% error for different number of tapers per trial
if (keeprpt == 4) && any(ntaper(:) ~= ntaper(1))
ft_error('currently you can only keep trials AND tapers, when using the number of tapers per frequency is equal across frequency')
end
% create tapfreqind for later indexing
freqtapind = [];
tempntaper = [0; cumsum(ntaper(:))];
for iindfoi = 1:numel(foi)
freqtapind{iindfoi} = tempntaper(iindfoi)+1:tempntaper(iindfoi+1);
end
case 'mtmfft'
[spectrum,ntaper,foi] = ft_specest_mtmfft(dat, time, 'taper', cfg.taper, options{:}, 'feedback', fbopt);
hastime = false;
case 'irasa'
[spectrum,ntaper,foi] = ft_specest_irasa(dat, time, options{:}, 'feedback', fbopt);
hastime = false;
case 'wavelet'
[spectrum,foi,toi] = ft_specest_wavelet(dat, time, 'timeoi', cfg.toi, 'width', cfg.width, 'gwidth', cfg.gwidth, options{:}, 'feedback', fbopt);
% the following variable is created to keep track of the number of
% trials per time bin and is needed for proper normalization if
% keeprpt==1 and the triallength is variable
if itrial==1, trlcnt = zeros(1, numel(foi), numel(toi)); end
hastime = true;
% create FAKE ntaper (this requires very minimal code change below for compatibility with the other specest functions)
ntaper = ones(1,numel(foi));
% modify spectrum for same reason as fake ntaper
spectrum = reshape(spectrum,[1 nchan numel(foi) numel(toi)]);
case 'superlet'
% calculate number of wavelets and respective cycle width dependent on superlet order
% equivalent one-liners:
% multiplicative: cycles = arrayfun(@(order) arrayfun(@(wl_num) cfg.width*wl_num, 1:order), cfg.order,'uni',0)
% additive: cycles = arrayfun(@(order) arrayfun(@(wl_num) cfg.width+wl_num-1, 1:order), cfg.order,'uni',0)
cycles = cell(length(cfg.foi),1);
for i_f = 1:length(cfg.foi)
frq_cyc = NaN(1,cfg.order(i_f));
if strcmp(cfg.combine, 'multiplicative')
for i_wl = 1:cfg.order(i_f)
frq_cyc(i_wl) = cfg.width*i_wl;
end
elseif strcmp(cfg.combine, 'additive')
for i_wl = 1:cfg.order(i_f)
frq_cyc(i_wl) = cfg.width+i_wl-1;
end
end
cycles{i_f} = frq_cyc;
end
% compute superlets
spectrum = NaN(nchan,length(cfg.foi),length(cfg.toi));
% index of 'freqoi' value in 'options'
idx_freqoi = find(ismember(options(1:2:end), 'freqoi'))*2;
foi = options{idx_freqoi};
for i_f = 1:length(cfg.foi)
% collext individual wavelets' responses per frequency
spec_f = NaN(cfg.order(i_f), nchan, length(cfg.toi));
opt = options;
opt{idx_freqoi} = cfg.foi(i_f);
% compute responses for individual wavelets
for i_wl = 1:cfg.order(i_f)
[spec_f(i_wl,:,:), dum, toi] = ft_specest_wavelet(dat, time, 'timeoi', cfg.toi, 'width', cycles{i_f}(i_wl), 'gwidth', cfg.gwidth, opt{:}, 'feedback', fbopt);
end
% geometric mean across individual wavelets
spectrum(:,i_f,:) = prod(spec_f, 1).^(1/cfg.order(i_f));
end
clear spec_f
% the following variable is created to keep track of the number of
% trials per time bin and is needed for proper normalization if
% keeprpt==1 and the triallength is variable
if itrial==1, trlcnt = zeros(1, numel(foi), numel(toi)); end
hastime = true;
% create FAKE ntaper (this requires very minimal code change below for compatibility with the other specest functions)
ntaper = ones(1,numel(foi));
% modify spectrum for same reason as fake ntaper
spectrum = reshape(spectrum,[1 nchan numel(foi) numel(toi)]);
case 'tfr'
[spectrum,foi,toi] = ft_specest_tfr(dat, time, 'timeoi', cfg.toi, 'width', cfg.width, 'gwidth', cfg.gwidth,options{:}, 'feedback', fbopt);
% the following variable is created to keep track of the number of
% trials per time bin and is needed for proper normalization if
% keeprpt==1 and the triallength is variable
if itrial==1, trlcnt = zeros(1, numel(foi), numel(toi)); end
hastime = true;
% create FAKE ntaper (this requires very minimal code change below for compatibility with the other specest functions)
ntaper = ones(1,numel(foi));
% modify spectrum for same reason as fake ntaper
spectrum = reshape(spectrum,[1 nchan numel(foi) numel(toi)]);
case 'hilbert'
[spectrum,foi,toi] = ft_specest_hilbert(dat, time, 'timeoi', cfg.toi, 'width', cfg.width, bpfiltoptions{:}, options{:}, 'feedback', fbopt, 'edgeartnan', cfg.edgeartnan);
hastime = true;
% create FAKE ntaper (this requires very minimal code change below for compatibility with the other specest functions)
ntaper = ones(1,numel(foi));
% modify spectrum for same reason as fake ntaper
spectrum = reshape(spectrum,[1 nchan numel(foi) numel(toi)]);
case 'neuvar'
[spectrum,foi] = ft_specest_neuvar(dat, time, options{:}, 'feedback', fbopt);
hastime = false;
% create FAKE ntaper (this requires very minimal code change below for compatibility with the other specest functions)
ntaper = ones(1,numel(foi));
end % switch
% Set n's
maxtap = max(ntaper);
nfoi = numel(foi);
if hastime
ntoi = numel(toi);
else
ntoi = 1; % this makes the same code compatible for hastime = false, as time is always the last dimension, and if singleton will disappear
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Memory allocation
if strcmp(cfg.method, 'mtmfft') && strcmp(cfg.taper, 'dpss')
% memory allocation for mtmfft is slightly different because of the possiblity of
% variable number of tapers over trials (when using dpss), the below exception is
% made so memory can still be allocated fully (see bug #1025
trllength = cellfun(@numel,data.time);
% determine number of tapers per trial
ntaptrl = sum(floor((2 .* (trllength./data.fsample) .* cfg.tapsmofrq) - 1)); % I floored it for now, because I don't know whether this formula is accurate in all cases, by flooring the memory allocated
% will most likely be less than it should be, but this would still have the same effect of 'not-crashing-matlabs'.
% I do have the feeling a round would be 100% accurate, but atm I cannot check this in Percival and Walden
% - roevdmei
else
ntaptrl = ntrials .* maxtap; % the way it used to be in all cases (before bug #1025)
end
% by default, everything is has the time dimension, if not, some specifics are performed
if itrial == 1
% allocate memory to output variables
if keeprpt == 1 % cfg.keeptrials, 'no' && cfg.keeptapers, 'no'
if powflg, powspctrm = zeros(nchan,nfoi,ntoi,cfg.precision); end
if csdflg, crsspctrm = complex(zeros(nchancmb,nfoi,ntoi,cfg.precision)); end
if fftflg, fourierspctrm = complex(zeros(nchan,nfoi,ntoi,cfg.precision)); end
dimord = 'chan_freq_time';
elseif keeprpt == 2 % cfg.keeptrials, 'yes' && cfg.keeptapers, 'no'
if powflg, powspctrm = nan(ntrials,nchan,nfoi,ntoi,cfg.precision); end
if csdflg, crsspctrm = complex(nan(ntrials,nchancmb,nfoi,ntoi,cfg.precision),nan(ntrials,nchancmb,nfoi,ntoi,cfg.precision)); end
if fftflg, fourierspctrm = complex(nan(ntrials,nchan,nfoi,ntoi,cfg.precision),nan(ntrials,nchan,nfoi,ntoi,cfg.precision)); end
dimord = 'rpt_chan_freq_time';
elseif keeprpt == 4 % cfg.keeptrials, 'yes' && cfg.keeptapers, 'yes'
if powflg, powspctrm = zeros(ntaptrl,nchan,nfoi,ntoi,cfg.precision); end %
if csdflg, crsspctrm = complex(zeros(ntaptrl,nchancmb,nfoi,ntoi,cfg.precision)); end
if fftflg, fourierspctrm = complex(zeros(ntaptrl,nchan,nfoi,ntoi,cfg.precision)); end
dimord = 'rpttap_chan_freq_time';
end
if ~hastime
dimord = dimord(1:end-5); % cut _time
end
% prepare calcdof
if strcmp(cfg.calcdof, 'yes')
if hastime
dof = zeros(nfoi,ntoi);
%dof = zeros(ntrials,nfoi,ntoi);
else
dof = zeros(nfoi,1);
%dof = zeros(ntrials,nfoi);
end
end
% prepare cumtapcnt
switch cfg.method %% IMPORTANT, SHOULD WE KEEP THIS SPLIT UP PER METHOD OR GO FOR A GENERAL SOLUTION NOW THAT WE HAVE SPECEST
case 'mtmconvol'
cumtapcnt = zeros(ntrials,nfoi);
case 'mtmfft'
cumtapcnt = zeros(ntrials,1);
end
end % itrial==1
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Create output
if keeprpt~=4
% mtmconvol is a special case and needs special processing
if strcmp(cfg.method, 'mtmconvol')
foiind = ones(1,nfoi);
else
% by using this vector below for indexing, the below code does not need to be duplicated for mtmconvol
foiind = 1:nfoi;
end
for ifoi = 1:nfoi
if strcmp(cfg.method, 'mtmconvol')
spectrum = reshape(permute(spectrum_mtmconvol(:,:,freqtapind{ifoi}),[3 1 2]),[ntaper(ifoi) nchan 1 ntoi]);
end
% set ingredients for below
if ~hastime
acttboi = 1;
nacttboi = 1;
else
acttboi = ~all(isnan(spectrum(1,:,foiind(ifoi),:)), 2); % check over all channels, some channels might contain a NaN
acttboi = reshape(acttboi, [1 ntoi]); % size(spectrum) = [? nchan nfoi ntoi]
nacttboi = sum(acttboi);
end
acttap = logical([ones(ntaper(ifoi),1);zeros(size(spectrum,1)-ntaper(ifoi),1)]);
if powflg
if strcmp(cfg.method, 'irasa') % ft_specest_irasa outputs power and not amplitude
powdum = spectrum(acttap,:,foiind(ifoi),acttboi);
else
powdum = abs(spectrum(acttap,:,foiind(ifoi),acttboi)) .^2;
end
% sinetaper scaling is disabled, because it is not consistent with the other
% tapers. if scaling is required, please specify cfg.taper =
% 'sine_old'
% if isfield(cfg, 'taper') && strcmp(cfg.taper, 'sine')
% %sinetapscale = zeros(ntaper(ifoi),nfoi); % assumes fixed number of tapers
% sinetapscale = zeros(ntaper(ifoi),1); % assumes fixed number of tapers
% for isinetap = 1:ntaper(ifoi) % assumes fixed number of tapers
% sinetapscale(isinetap,:) = (1 - (((isinetap - 1) ./ ntaper(ifoi)) .^ 2));
% end
% sinetapscale = reshape(repmat(sinetapscale,[1 1 nchan ntoi]),[ntaper(ifoi) nchan 1 ntoi]);
% powdum = powdum .* sinetapscale;
% end
end
if fftflg
fourierdum = spectrum(acttap,:,foiind(ifoi),acttboi);
end
if csdflg
csddum = spectrum(acttap,cutdatindcmb(:,1),foiind(ifoi),acttboi) .* conj(spectrum(acttap,cutdatindcmb(:,2),foiind(ifoi),acttboi));
end
% switch between keep's
switch keeprpt
case 1 % cfg.keeptrials, 'no' && cfg.keeptapers, 'no'
if ~isempty(trlcnt)
trlcnt(1, ifoi, :) = trlcnt(1, ifoi, :) + shiftdim(double(acttboi(:)'),-1);
end
if powflg
powspctrm(:,ifoi,acttboi) = powspctrm(:,ifoi,acttboi) + (reshape(mean(powdum,1),[nchan 1 nacttboi]) ./ ntrials);
%powspctrm(:,ifoi,~acttboi) = NaN;
end
if fftflg
fourierspctrm(:,ifoi,acttboi) = fourierspctrm(:,ifoi,acttboi) + (reshape(mean(fourierdum,1),[nchan 1 nacttboi]) ./ ntrials);
%fourierspctrm(:,ifoi,~acttboi) = NaN;
end
if csdflg
crsspctrm(:,ifoi,acttboi) = crsspctrm(:,ifoi,acttboi) + (reshape(mean(csddum,1),[nchancmb 1 nacttboi]) ./ ntrials);
%crsspctrm(:,ifoi,~acttboi) = NaN;
end
case 2 % cfg.keeptrials, 'yes' && cfg.keeptapers, 'no'
if powflg
powspctrm(itrial,:,ifoi,acttboi) = reshape(mean(powdum,1),[nchan 1 nacttboi]);
powspctrm(itrial,:,ifoi,~acttboi) = NaN;
end
if fftflg
fourierspctrm(itrial,:,ifoi,acttboi) = reshape(mean(fourierdum,1), [nchan 1 nacttboi]);
fourierspctrm(itrial,:,ifoi,~acttboi) = NaN;
end
if csdflg
crsspctrm(itrial,:,ifoi,acttboi) = reshape(mean(csddum,1), [nchancmb 1 nacttboi]);
crsspctrm(itrial,:,ifoi,~acttboi) = NaN;
end
end % switch keeprpt
% do calcdof dof = zeros(numper,numfoi,numtoi);
if strcmp(cfg.calcdof, 'yes')
if hastime
acttimboiind = ~all(isnan(spectrum(1,:,foiind(ifoi),:)), 2); % check over all channels, some channels might contain a NaN
acttimboiind = reshape(acttimboiind, [1 ntoi]);
dof(ifoi,acttimboiind) = ntaper(ifoi) + dof(ifoi,acttimboiind);
else % hastime = false
dof(ifoi) = ntaper(ifoi) + dof(ifoi);
end
end
end %ifoi
else
% keep tapers
if ~exist('tapcounter', 'var')
tapcounter = 0;
end
if strcmp(cfg.method, 'mtmconvol')
spectrum = permute(reshape(spectrum_mtmconvol,[nchan ntoi ntaper(1) nfoi]),[3 1 4 2]);
end
currrptind = tapcounter + (1:maxtap);
tapcounter = currrptind(end);
%rptind = reshape(1:ntrials .* maxtap,[maxtap ntrials]);
%currrptind = rptind(:,itrial);
if powflg
if strcmp(cfg.method, 'irasa') % ft_specest_irasa outputs power and not amplitude
powspctrm(currrptind,:,:) = spectrum;
else
powspctrm(currrptind,:,:) = abs(spectrum).^2;
end
end
if fftflg
fourierspctrm(currrptind,:,:,:) = spectrum;
end
if csdflg
crsspctrm(currrptind,:,:,:) = spectrum(cutdatindcmb(:,1),:,:) .* ...
conj(spectrum(cutdatindcmb(:,2),:,:));
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% set cumptapcnt
switch cfg.method %% IMPORTANT, SHOULD WE KEEP THIS SPLIT UP PER METHOD OR GO FOR A GENERAL SOLUTION NOW THAT WE HAVE SPECEST
case {'mtmconvol' 'wavelet'}
cumtapcnt(itrial,:) = ntaper;
case 'mtmfft'
cumtapcnt(itrial,1) = ntaper(1); % fixed number of tapers? for the moment, yes, as specest_mtmfft computes only one set of tapers
end
end % for ntrials
ft_progress('close');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% END: Main loop over trials
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% re-normalise the TFRs if keeprpt==1
if (strcmp(cfg.method, 'mtmconvol') || strcmp(cfg.method, 'wavelet')) && keeprpt==1
nanmask = trlcnt==0;
if powflg
powspctrm = powspctrm.*ntrials;
powspctrm = powspctrm./trlcnt(ones(size(powspctrm,1),1),:,:);
powspctrm(nanmask(ones(size(powspctrm,1),1),:,:)) = nan;
end
if fftflg
fourierspctrm = fourierspctrm.*ntrials;
fourierspctrm = fourierspctrm./trlcnt(ones(size(fourierspctrm,1),1),:,:);
fourierspctrm(nanmask(ones(size(fourierspctrm,1),1),:,:)) = nan;
end
if csdflg
crsspctrm = crsspctrm.*ntrials;
crsspctrm = crsspctrm./trlcnt(ones(size(crsspctrm,1),1),:,:);
crsspctrm(nanmask(ones(size(crsspctrm,1),1),:,:)) = nan;
end
end
% set output variables
freq = [];
freq.label = data.label;
freq.dimord = dimord;
freq.freq = foi;
hasdc = find(foi==0);
hasnyq = find(foi==data.fsample./2);
hasdc_nyq = [hasdc hasnyq];
if exist('toi', 'var')
freq.time = toi;
end
if powflg
% correct the 0 Hz or Nyqist bin if present, scaling with a factor of 2 is only appropriate for ~0 Hz
if ~isempty(hasdc_nyq)
if keeprpt>1
powspctrm(:,:,hasdc_nyq,:) = powspctrm(:,:,hasdc_nyq,:)./2;
else
powspctrm(:,hasdc_nyq,:) = powspctrm(:,hasdc_nyq,:)./2;
end
end
if startsWith(cfg.output, 'fooof')
% check for brainstorm functions on the path, and add if needed
ft_hastoolbox('brainstorm', 1);
TF(:,1,:) = powspctrm;
Freqs = freq.freq;
Freqs(Freqs==0) = [];
% This grabs the defaults from the brainstorm code
opts_bst = getfield(process_fooof('GetDescription'), 'options');
% Fetch user settings, this is a chunk of code copied over from
% process_fooof, to bypass the whole database etc handling.