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Global neural oscillations underlie performance variability and attentional state fluctuations in humans.

Code ↔ Paper

4 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 4 matches
  1. [1] § Methods › Aperiodic component analysis ↔ freq_lan.m, lines 1–136 · score 0.70 · cfg.tapsmofrq, multi taper, frequency smoothing, Hz
  2. [2] § Methods › Aperiodic component analysis ↔ fieltrip/freq_mtmconvol_lan.m, lines 1–61 · score 0.68 · cfg.tapsmofrq, multi taper, DPSS, Sequences, smoothing, spectral
  3. [3] § Methods › Aperiodic component analysis ↔ fieltrip/freq_mtmconvol_lan.m, lines 1–61 · score 0.58 · power spectra, oscillatory activity, freqanalysis, preprocessed
  4. [4] § Methods › Anatomical localization and classification of electrode contacts ↔ iEEG/lan_setref_micromed.m, lines 422–541 · score 0.53 · co registered, SPM, location, electrodes

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 553 lines · 21 KB · no license · 2 matches

  1. function [freq] = freq_mtmconvol_lan(cfg, LAN, texto)
  2. % v.0.1.2
  3. % analysis multipater for LAN structur
  4. % adapted of filetrip scripts
  5. %
  6. %
  7. %
  8. %
  9. %
  10. % Original HELP por freqanalysis_mtmconvol.m
  11. %
  12. % FREQANALYSIS_MTMCONVOL performs time-frequency analysis on any time series trial data
  13. % using the 'multitaper method' (MTM) based on Slepian sequences as tapers. Alternatively,
  14. % you can use conventional tapers (e.g. Hanning).
  15. %
  16. % Use as
  17. % [freq] = freqanalysis(cfg, data)
  18. %
  19. % The data should be organised in a structure as obtained from
  20. % the PREPROCESSING function. The configuration should be according to
  21. % cfg.method = method used for frequency or time-frequency decomposition
  22. % see FREQANALYSIS for details
  23. % cfg.output = 'pow' return the power-spectra
  24. % 'powandcsd' return the power and the cross-spectra
  25. % 'fourier' return the complex Fourier-spectra
  26. % cfg.taper = 'dpss', 'hanning' or many others, see WINDOW (default = 'dpss')
  27. %
  28. % For cfg.output='powandcsd', you should specify the channel combinations
  29. % between which to compute the cross-spectra as cfg.channelcmb. Otherwise
  30. % you should specify only the channels in cfg.channel.
  31. %
  32. % cfg.channel = Nx1 cell-array with selection of channels (default = 'all'),
  33. % see CHANNELSELECTION for details
  34. % cfg.channelcmb = Mx2 cell-array with selection of channel pairs (default = {'all' 'all'}),
  35. % see CHANNELCOMBINATION for details
  36. % cfg.foi = vector 1 x numfoi, frequencies of interest
  37. % cfg.t_ftimwin = vector 1 x numfoi, length of time window (in seconds)
  38. % cfg.tapsmofrq = vector 1 x numfoi, the amount of spectral smoothing through
  39. % multi-tapering. Note that 4 Hz smoothing means
  40. % plus-minus 4 Hz, i.e. a 8 Hz smoothing box.
  41. % cfg.toi = vector 1 x numtoi, the times on which the analysis windows
  42. % should be centered (in seconds)
  43. % cfg.trials = 'all' or a selection given as a 1xN vector (default = 'all')
  44. % cfg.keeptrials = 'yes' or 'no', return individual trials or average (default = 'no')
  45. % cfg.keeptapers = 'yes' or 'no', return individual tapers or average (default = 'no')
  46. % cfg.pad = number or 'maxperlen', length in seconds to which the data can be padded out (default = 'maxperlen')
  47. %
  48. % The padding will determine your spectral resolution. If you want to
  49. % compare spectra from data pieces of different lengths, you should use
  50. % the same cfg.pad for both, in order to spectrally interpolate them to
  51. % the same spectral resolution. Note that this will run very slow if you
  52. % specify cfg.pad as maxperlen AND the number of samples turns out to have
  53. % a large prime factor sum. This is because the FFTs will then be computed
  54. % very inefficiently.
  55. %
  56. % An asymmetric taper usefull for TMS is used when cfg.taper='alpha' and corresponds to
  57. % W. Kyle Mitchell, Mark R. Baker & Stuart N. Baker. Muscle Responses to
  58. % Transcranial Stimulation Depend on Background Oscillatory Activity.
  59. % published online Jul 12, 2007 J. Physiol.
  60. %
  61. % See also FREQANALYSIS
  63. %fieldtripdefs
  65. if nargin < 3
  66. texto = plus_text();
  67. end
  68. texto = plus_text(texto, ' ');
  70. if isfield(LAN,'freq')
  71. freq = LAN.freq
  72. end
  74. %
  75. data.trial = LAN.data;
  76. time = LAN.time;
  77. % evaluar bien esto
  78. %data.offset = ones(1,length(LAN.data)) * -300;%time(:,1)' +
  79. %data.offset = round(time(1)*LAN.srate);
  80. %data.offset = LAN.time(:,2);
  82. LAN.time =[];
  83. for t = 1:length(LAN.data)
  84. LAN.time{t} = linspace(time(t,1) , time(t,2) , length(LAN.data{t}));
  85. data.offset(t) = round(LAN.time{t}(1)*LAN.srate);
  86. end
  87. data.fsample = LAN.srate;
  88. LAN.data = [];
  89. %
  90. for l = 1:LAN.nbchan%length( LAN.chanlocs )
  91. try
  92. data.label{l,1} = LAN.chanlocs(l).labels;
  93. catch
  94. data.label{l,1} = num2str(l);
  95. end
  97. end
  99. % temporal
  100. %cfg = []
  102. % set all the defaults
  103. if ~isfield(cfg, 'method'), cfg.method = 'mtmconvol'; end
  104. if ~isfield(cfg, 'keeptapers'), cfg.keeptapers = 'no'; end
  105. if ~isfield(cfg, 'keeptrials'), cfg.keeptrials = 'no'; end
  106. if ~isfield(cfg, 'calcdof'), cfg.calcdof = 'no'; end
  107. if ~isfield(cfg, 'output'), cfg.output = 'powandcsd'; end
  108. if ~isfield(cfg, 'pad'), cfg.pad = 'maxperlen'; end
  109. if ~isfield(cfg, 'taper'), cfg.taper = 'dpss'; end
  110. if ~isfield(cfg, 'channel'), cfg.channel = 'all'; end
  111. if strcmp(cfg.output, 'fourier'),
  112. cfg.keeptrials = 'yes';
  113. cfg.keeptapers = 'yes';
  114. end
  116. % setting a flag (csdflg) that determines whether this routine outputs
  117. % only power-spectra or power-spectra and cross-spectra?
  118. if strcmp(cfg.output,'pow')
  119. powflg = 1;
  120. csdflg = 0;
  121. fftflg = 0;
  122. elseif strcmp(cfg.output,'powandcsd')
  123. powflg = 1;
  124. csdflg = 1;
  125. fftflg = 0;
  126. elseif strcmp(cfg.output,'fourier')
  127. powflg = 0;
  128. csdflg = 0;
  129. fftflg = 1;
  130. else
  131. error('Unrecognized output required');
  132. end
  134. if ~isfield(cfg, 'channelcmb') && csdflg
  135. %set the default for the channelcombination
  136. cfg.channelcmb = {'all' 'all'};
  137. elseif isfield(cfg, 'channelcmb') && ~csdflg
  138. % no cross-spectrum needs to be computed, hence remove the combinations from cfg
  139. cfg = rmfield(cfg, 'channelcmb');
  140. end
  143. %data.label = zeros(length( LAN.chanlocs ),1);
  147. % ensure that channelselection and selection of channelcombinations is
  148. % perfomed consistently
  151. %cfg.channel = channelselection_lan(cfg.channel, data.label);
  153. if strcmp(cfg.channel,'all')
  154. for l = 1:length(data.label)
  155. a(l) = data.label(l) ;
  156. end
  157. cfg.channel = a;
  158. end
  159. if isfield(cfg, 'channelcmb')
  160. warning('Falta arreglar compatibilidad LAN-Fieltrid para esta funcion');
  161. cfg.channelcmb = channelcombination_lan(cfg.channelcmb, data.label);
  162. end
  164. % determine the corresponding indices of all channels
  165. %
  166. %
  169. sgnindx = match_str_lan(data.label, cfg.channel);
  170. numsgn = size(sgnindx,1);
  172. if csdflg
  173. % determine the corresponding indices of all channel combinations
  174. sgncmbindx = zeros(size(cfg.channelcmb));
  175. for k=1:size(cfg.channelcmb,1)
  176. sgncmbindx(k,1) = strmatch(cfg.channelcmb(k,1), data.label, 'exact');
  177. sgncmbindx(k,2) = strmatch(cfg.channelcmb(k,2), data.label, 'exact');
  178. end
  180. numsgncmb = size(sgncmbindx,1);
  181. sgnindx = unique([sgnindx(:); sgncmbindx(:)]);
  182. numsgn = length(sgnindx);
  184. cutdatindcmb = zeros(size(sgncmbindx));
  185. for sgnlop = 1:numsgn
  186. cutdatindcmb(find(sgncmbindx == sgnindx(sgnlop))) = sgnlop;
  187. end
  188. end
  191. % if rectan is 1 it means that trials are of equal lengths
  192. numper = numel(data.trial);
  193. %numper = numel(LAN.data);
  194. %
  195. numdatbnsarr = zeros(numper, 1);
  196. for perlop = 1:numper
  197. numdatbnsarr(perlop) = size(data.trial{perlop},2);
  198. end
  199. rectan = all(numdatbnsarr==numdatbnsarr(1));
  204. % if cfg.pad is 'maxperlen', this is realized here:
  205. % first establish where the first possible sample is
  206. min_smp = min(data.offset);
  207. % then establish where the last possible sample is
  208. max_smp = max(numdatbnsarr(:)+data.offset(:));
  209. if isequal(cfg.pad, 'maxperlen')
  210. % pad the data from the first possible to last possible sample
  211. cfg.pad = (max_smp-min_smp) ./ data.fsample;
  212. else
  213. % check that the specified padding is not too short
  214. if cfg.pad<((max_smp-min_smp)/data.fsample)
  215. error('the padding that you specified is shorter than the longest trial in the data');
  216. end
  217. end
  218. clear min_smp max_smp
  219. numsmp = round(cfg.pad .* data.fsample);
  221. % keeping trials and/or tapers?
  222. if strcmp(cfg.keeptrials,'no') && strcmp(cfg.keeptapers,'no')
  223. keep = 1;
  224. elseif strcmp(cfg.keeptrials,'yes') && strcmp(cfg.keeptapers,'no')
  225. keep = 2;
  226. elseif strcmp(cfg.keeptrials,'no') && strcmp(cfg.keeptapers,'yes')
  227. error('There is currently no support for keeping tapers WITHOUT KEEPING TRIALS.');
  228. elseif strcmp(cfg.keeptrials,'yes') && strcmp(cfg.keeptapers,'yes')
  229. keep = 4;
  230. end
  232. if strcmp(cfg.keeptrials,'yes') && strcmp(cfg.keeptapers,'yes')
  233. if ~strcmp(cfg.output, 'fourier'),
  234. error('Keeping trials AND tapers is only possible with fourier as the output.');
  235. elseif strcmp(cfg.taper, 'dpss') && ~(all(cfg.tapsmofrq==cfg.tapsmofrq(1)) && all(cfg.t_ftimwin==cfg.t_ftimwin(1))),
  236. error('Currently you can only keep trials AND tapers, when using the number of tapers per frequency is equal across frequency');
  237. end
  238. end
  240. if strcmp(cfg.taper, 'alpha') && ~all(cfg.t_ftimwin==cfg.t_ftimwin(1))
  241. error('you can only use alpha tapers with an cfg.t_ftimwin that is equal for all frequencies');
  242. end
  244. %
  245. minoffset = min(data.offset);
  246. %minoffset = min(LAN.time(:,2));
  247. %
  248. %
  250. timboi = round(cfg.toi .* data.fsample - minoffset);
  251. toi = round(cfg.toi .* data.fsample) ./ data.fsample;
  252. numtoi = length(cfg.toi);
  253. numfoi = length(cfg.foi);
  254. numtap = zeros(numfoi,1);
  256. % calculating degrees of freedom
  257. calcdof = strcmp(cfg.calcdof,'yes');
  258. if calcdof
  259. dof = zeros(numper,numfoi,numtoi);
  260. end;
  262. % compute the tapers and their fft
  263. knlspctrmstr = cell(numfoi,1);
  264. for foilop = 1:numfoi
  265. acttapnumsmp = round(cfg.t_ftimwin(foilop) .* data.fsample);
  266. if strcmp(cfg.taper, 'dpss')
  267. % create a sequence of DPSS (Slepian) tapers, ensure that the input arguments are double
  268. tap = double_dpss(acttapnumsmp, acttapnumsmp .* (cfg.tapsmofrq(foilop)./data.fsample));
  269. %
  270. elseif strcmp(cfg.taper, 'sine')
  271. tap = sine_taper(acttapnumsmp, acttapnumsmp .* (cfg.tapsmofrq(foilop)./data.fsample));
  272. elseif strcmp(cfg.taper, 'alpha')
  273. tap = alpha_taper(acttapnumsmp, cfg.foi(foilop)./data.fsample);
  274. tap = tap./norm(tap);
  275. % freqanalysis_mtmconvol always throws away the last taper of the Slepian sequence, so add a dummy taper
  276. tap(:,2) = nan;
  277. else
  278. % create a single taper according to the window specification as a replacement for the DPSS (Slepian) sequence
  279. tap = window(cfg.taper, acttapnumsmp);
  280. tap = tap./norm(tap);
  281. % freqanalysis_mtmconvol always throws away the last taper of the Slepian sequence, so add a dummy taper
  282. tap(:,2) = nan;
  283. end
  285. %%
  287. numtap(foilop) = size(tap,2)-1;
  288. if (numtap(foilop) < 1)
  289. error(sprintf('%.3f Hz : datalength to short for specified smoothing\ndatalength: %.3f s, smoothing: %.3f Hz, minimum smoothing: %.3f Hz', cfg.foi(foilop), acttapnumsmp/data.fsample, cfg.tapsmofrq(foilop), data.fsample/acttapnumsmp));
  290. elseif (numtap(foilop) < 2) && strcmp(cfg.taper, 'dpss')
  291. fprintf('%.3f Hz : WARNING - using only one taper for specified smoothing\n',cfg.foi(foilop));
  292. end
  293. ins = ceil(numsmp./2) - floor(acttapnumsmp./2);
  294. prezer = zeros(ins,1);
  295. pstzer = zeros(numsmp - ((ins-1) + acttapnumsmp)-1,1);
  296. ind = (0:acttapnumsmp-1)' .* ((2.*pi./data.fsample) .* cfg.foi(foilop));
  297. knlspctrmstr{foilop} = complex(zeros(numtap(foilop),numsmp));
  298. for taplop = 1:numtap(foilop)
  299. try
  300. % construct the complex wavelet
  301. coswav = vertcat(prezer,tap(:,taplop).*cos(ind),pstzer);
  302. sinwav = vertcat(prezer,tap(:,taplop).*sin(ind),pstzer);
  303. wavelet = complex(coswav, sinwav);
  304. % store the fft of the complex wavelet
  305. knlspctrmstr{foilop}(taplop,:) = fft(wavelet,[],1)';
  306. global fb
  307. if ~isempty(fb) && fb
  308. % plot the wavelet for debugging
  309. figure
  310. plot(tap(:,taplop).*cos(ind), 'r'); hold on
  311. plot(tap(:,taplop).*sin(ind), 'g');
  312. plot(tap(:,taplop) , 'b');
  313. title(sprintf('taper %d @ %g Hz', taplop, cfg.foi(foilop)));
  314. drawnow
  315. end
  316. end
  317. end
  318. end
  320. if keep == 1
  321. if powflg, powspctrm = zeros(numsgn,numfoi,numtoi); end
  322. if csdflg, crsspctrm = complex(zeros(numsgncmb,numfoi,numtoi)); end
  323. if fftflg, fourierspctrm = complex(zeros(numsgn,numfoi,numtoi)); end
  324. cntpertoi = zeros(numfoi,numtoi);
  325. dimord = 'chan_freq_time';
  326. elseif keep == 2
  327. if powflg, powspctrm = zeros(numper,numsgn,numfoi,numtoi); end
  328. if csdflg, crsspctrm = complex(zeros(numper,numsgncmb,numfoi,numtoi)); end
  329. if fftflg, fourierspctrm = complex(zeros(numper,numsgn,numfoi,numtoi)); end
  330. dimord = 'rpt_chan_freq_time';
  331. elseif keep == 4
  332. % FIXME this works only if all frequencies have the same number of tapers
  333. if powflg, powspctrm = zeros(numper*numtap(1),numsgn,numfoi,numtoi); end
  334. if csdflg, crsspctrm = complex(zeros(numper*numtap(1),numsgncmb,numfoi,numtoi)); end
  335. if fftflg, fourierspctrm = complex(zeros(numper*numtap(1),numsgn,numfoi,numtoi)); end
  336. cnt = 0;
  337. dimord = 'rpttap_chan_freq_time';
  338. end
  340. texto = plus_text(texto,' ' );
  341. for perlop = 1:numper
  342. % LAN
  343. if isempty(data.trial{perlop})
  344. no_t = no_t + 1; % rejected trials!!
  345. continue
  346. end
  348. %fprintf('processing trial %d: %d samples\n', perlop, numdatbnsarr(perlop,1));
  349. texto = last_text(texto, ['processing trial ' num2str(perlop) ' :' num2str(numdatbnsarr(perlop,1)) ]);
  350. %display(['processing trial ' num2str(perlop) ' :' num2str(numdatbnsarr(perlop,1)) ]);
  351. clc, disp_lan(texto);
  352. if keep == 2
  353. cnt = perlop;
  354. end
  355. numdatbns = numdatbnsarr(perlop,1);
  356. % prepad = zeros(1,data.offset(perlop) - minoffset);
  357. % pstpad = zeros(1,minoffset + numsmp - (data.offset(perlop) + numdatbns));
  358. % datspctra = complex(zeros(numsgn,numsmp));
  359. % for sgnlop = 1:numsgn
  360. % datspctra(sgnlop,:) = fft([prepad, data.trial{perlop}(sgnindx(sgnlop),:), ...
  361. % pstpad],[],2);
  362. % end
  363. prepad = zeros(numsgn,data.offset(perlop) - minoffset);
  364. pstpad = zeros(numsgn,minoffset + numsmp - (data.offset(perlop) + numdatbns));
  365. tmp = data.trial{perlop}(sgnindx,:);
  366. tmp = [prepad tmp pstpad];
  367. % avoid the use of a 3rd input argument to facilitate compatibility with star-P
  368. % use explicit transpose, to avoid complex conjugate transpose
  369. datspctra = transpose(fft(transpose(tmp)));
  370. for foilop = 1:numfoi
  371. %-------------------
  372. % clc;
  373. %disp_lan(texto);
  374. if perlop==1
  375. fprintf('processing frequency %d (%.2f Hz), %d tapers\n', foilop,cfg.foi(foilop),numtap(foilop));
  376. end
  377. %--------------------
  378. actfoinumsmp = cfg.t_ftimwin(foilop) .* data.fsample;
  379. acttimboiind = find(timboi >= (-minoffset + data.offset(perlop) + (actfoinumsmp ./ 2)) & timboi < (-minoffset + data.offset(perlop) + numdatbns - (actfoinumsmp ./2)));
  380. nonacttimboiind = find(timboi < (-minoffset + data.offset(perlop) + (actfoinumsmp ./ 2)) | timboi >= (-minoffset + data.offset(perlop) + numdatbns - (actfoinumsmp ./2)));
  381. acttimboi = timboi(acttimboiind);
  382. numacttimboi = length(acttimboi);
  383. if keep ==1
  384. cntpertoi(foilop,acttimboiind) = cntpertoi(foilop,acttimboiind) + 1;
  385. end
  386. for taplop = 1:numtap(foilop)
  387. if keep == 3
  388. cnt = taplop;
  389. elseif keep == 4
  390. % this once again assumes a fixed number of tapers per frequency
  391. cnt = (perlop-1)*numtap(1) + taplop;
  392. end
  393. autspctrmacttap = complex(zeros(numsgn,numacttimboi), zeros(numsgn,numacttimboi));
  394. if numacttimboi > 0
  395. for sgnlop = 1:numsgn
  396. dum = fftshift(ifft(datspctra(sgnlop,:) .* knlspctrmstr{foilop}(taplop,:),[],2));
  397. autspctrmacttap(sgnlop,:) = dum(acttimboi);
  398. end
  399. end
  400. if powflg
  401. powdum = 2.* abs(autspctrmacttap) .^ 2 ./ actfoinumsmp;
  402. if strcmp(cfg.taper, 'sine')
  403. powdum = powdum .* (1 - (((taplop - 1) ./ numtap(foilop)) .^ 2));
  404. end
  405. if keep == 1 && numacttimboi > 0
  406. powspctrm(:,foilop,acttimboiind) = powspctrm(:,foilop,acttimboiind) + reshape(powdum ./ numtap(foilop),[numsgn,1,numacttimboi]);
  407. elseif keep == 2 && numacttimboi > 0
  408. powspctrm(cnt,:,foilop,acttimboiind) = powspctrm(cnt,:,foilop,acttimboiind) + reshape(powdum ./ numtap(foilop),[1,numsgn,1,numacttimboi]);
  409. powspctrm(cnt,:,foilop,nonacttimboiind) = nan;
  410. elseif keep == 4 && numacttimboi > 0
  411. powspctrm(cnt,:,foilop,acttimboiind) = reshape(powdum,[1,numsgn,1,numacttimboi]);
  412. powspctrm(cnt,:,foilop,nonacttimboiind) = nan;
  413. elseif (keep == 4 || keep == 2) && numacttimboi == 0
  414. powspctrm(cnt,:,foilop,nonacttimboiind) = nan;
  415. end
  416. end
  417. if fftflg
  418. fourierdum = (autspctrmacttap) .* sqrt(2 ./ actfoinumsmp); %cf Numercial Receipes 13.4.9
  419. if keep == 1 && numacttimboi > 0
  420. fourierspctrm(:,foilop,acttimboiind) = fourierspctrm(:,foilop,acttimboiind) + reshape((fourierdum ./ numtap(foilop)),[numsgn,1,numacttimboi]);
  421. elseif keep == 2 && numacttimboi > 0
  422. fourierspctrm(cnt,:,foilop,acttimboiind) = fourierspctrm(cnt,:,foilop,acttimboiind) + reshape(fourierdum ./ numtap(foilop),[1,numsgn,1,numacttimboi]);
  423. fourierspctrm(cnt,:,foilop,nonacttimboiind) = nan;
  424. elseif keep == 4 && numacttimboi > 0
  425. fourierspctrm(cnt,:,foilop,acttimboiind) = reshape(fourierdum,[1,numsgn,1,numacttimboi]);
  426. fourierspctrm(cnt,:,foilop,nonacttimboiind) = nan;
  427. elseif (keep == 4 || keep == 2) && numacttimboi == 0
  428. fourierspctrm(cnt,:,foilop,nonacttimboiind) = nan;
  429. end
  430. end
  431. if csdflg
  432. csddum = 2.* (autspctrmacttap(cutdatindcmb(:,1),:) .* conj(autspctrmacttap(cutdatindcmb(:,2),:))) ./ actfoinumsmp;
  433. if keep == 1 && numacttimboi > 0
  434. crsspctrm(:,foilop,acttimboiind) = crsspctrm(:,foilop,acttimboiind) + reshape((csddum ./ numtap(foilop)),[numsgncmb,1,numacttimboi]);
  435. elseif keep == 2 && numacttimboi > 0
  436. crsspctrm(cnt,:,foilop,acttimboiind) = crsspctrm(cnt,:,foilop,acttimboiind) + reshape(csddum ./ numtap(foilop),[1,numsgncmb,1,numacttimboi]);
  437. crsspctrm(cnt,:,foilop,nonacttimboiind) = nan;
  438. elseif keep == 4 && numacttimboi > 0
  439. crsspctrm(cnt,:,foilop,acttimboiind) = reshape(csddum,[1,numsgncmb,1,numacttimboi]);
  440. crsspctrm(cnt,:,foilop,nonacttimboiind) = nan;
  441. elseif (keep == 4 || keep == 2) && numacttimboi == 0
  442. crsspctrm(cnt,:,foilop,nonacttimboiind) = nan;
  443. end
  444. end
  445. end % for taplop
  446. if calcdof
  447. dof(perlop,foilop,acttimboiind) = numtap(foilop);
  448. end
  449. end % for foilop
  450. end % for perlop
  452. if keep == 1
  453. warning off
  454. if powflg
  455. powspctrm(:,:,:) = powspctrm(:,:,:) ./ repmat(permute(cntpertoi,[3,1,2]),[numsgn,1,1]);
  456. end
  457. if fftflg
  458. fourierspctrm(:,:,:) = fourierspctrm(:,:,:) ./ repmat(permute(cntpertoi,[3,1,2]),[numsgn,1,1]);
  459. end
  460. if csdflg
  461. crsspctrm(:,:,:) = crsspctrm(:,:,:) ./ repmat(permute(cntpertoi,[3,1,2]),[numsgncmb,1,1]);
  462. end
  463. warning on
  464. end
  466. % collect the results
  467. freq.label = data.label(sgnindx);
  468. freq.dimord = dimord;
  469. freq.freq = cfg.foi;
  470. freq.time = toi;
  472. if powflg
  473. if keep ==2
  474. for tt = 1:size(powspctrm,1)
  475. if LAN.accept(tt) %LAN
  476. freq.powspctrm{tt} = single(permute(powspctrm(tt,:,:,:),[3,2,4,1]));
  477. else %LAN
  478. freq.powspctrm{tt} = [ ];
  479. end
  480. end
  481. % save in file
  482. % LAN !!!
  483. if strcmp(cfg.ktt,'file')
  484. paso = what ;
  486. filename = [LAN.name '_' LAN.cond '_' LAN.group '_' datestr(now,'HHMMSS_dd_mm_yy') '.ldt'];
  487. filename = strrep(filename, ' ' , '_');
  488. t_powspctrm = freq.powspctrm;
  489. m_powspctrm = mean(cat(4,freq.powspctrm{:}),4);
  490. save(filename,'t_powspctrm','m_powspctrm');
  491. clear t_powspctrm m_powspctrm
  492. freq.powspctrm = [];
  493. freq.powspctrm.filename = filename;
  494. freq.powspctrm.path = paso.path;
  495. freq.powspctrm.trials = 't_powspctrm';
  496. freq.powspctrm.mean = 'm_powspctrm';
  497. clear paso
  499. end
  500. else
  501. freq.powspctrm = permute(powspctrm,[2,1,3]);
  502. end
  503. %
  504. end
  506. if csdflg
  507. freq.labelcmb = cfg.channelcmb;
  508. freq.crsspctrm = crsspctrm;
  509. end
  511. if fftflg
  512. freq.fourierspctrm = fourierspctrm;
  513. end
  515. if calcdof
  516. freq.dof=2*dof;
  517. end;
  519. if keep == 2,
  520. freq.cumtapcnt = repmat(numtap(:)', [size(powspctrm,1) 1]);
  521. elseif keep == 4,
  522. %all(numtap(1)==numtap)
  523. freq.cumtapcnt = repmat(numtap(1), [size(fourierspctrm,1)./numtap(1) 1]);
  524. end
  526. try, freq.grad = data.grad; end % remember the gradiometer array
  527. try, freq.elec = data.elec; end % remember the electrode array
  529. % get the output cfg
  530. %cfg = checkconfig(cfg, 'trackconfig', 'off', 'checksize', 'yes');
  532. % add information about the version of this function to the configuration
  533. try
  534. % get the full name of the function
  535. cfg.version.name = mfilename('fullpath');
  536. catch
  537. % required for compatibility with Matlab versions prior to release 13 (6.5)
  538. [st, i1] = dbstack;
  539. cfg.version.name = st(i1);
  540. end
  541. cfg.version.id = '$Id: freqanalysis_mtmconvol.m,v 1.44 2009/03/11 10:39:37 roboos Exp $';
  542. % remember the configuration details of the input data
  543. try cfg.previous = data.cfg; end
  544. % remember the exact configuration details in the output
  545. freq.cfg = cfg;
  547. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  548. % SUBFUNCTION ensure that the first two input arguments are of double
  549. % precision this prevents an instability (bug) in the computation of the
  550. % tapers for Matlab 6.5 and 7.0
  551. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  552. function [tap] = double_dpss(a, b, varargin);
  553. tap = dpss(double(a), double(b), varargin{:});

freq_mtmconvol_lan.m at commit afb0611, no license · at the source

Overview

Authors: Joaquín Herrero1,2,3, Rodrigo Henríquez-Ch1,4,2,5,6, Alejandra Figueroa-Vargas7, Reinaldo Uribe-San Martin1,4,5, Christian Cantillano1,8,5, Pablo Fuentealba1,9,5,10, Patricio Mellado1,4,5, Jaime Godoy1,4,5, Pablo Billeke7, Francisco Aboitiz1,9,2,5,6
ORCID iDs: Joaquín Herrero, Rodrigo Henríquez-Ch, Alejandra Figueroa-Vargas
  1. Centro Interdisciplinario de Neurociencia, Facultad de Medicina, Pontificia Universidad Católica de Chile,Santiago, Chile
  2. Laboratorio LaNCE, Facultad de Medicina, Pontificia Universidad Católica de Chile,Santiago, Chile
  3. Escuela de Kinesiología, Facultad de Medicina y Salud, Universidad Finis Terrae,Santiago, Chile
  4. Departamento de Neurología, Facultad de Medicina, Pontificia Universidad Católica de Chile,Santiago, Chile
  5. Escuela de Medicina, Pontificia Universidad Católica de Chile,Santiago, Chile
  6. Centro ANID de Interés Nacional para Investigación e Innovación en Niñez, Adolescencia, Resiliencia y Adversidad, CIN 250068, IINARA,Santiago, Chile
  7. Centro de Investigación en Complejidad Social, Facultad de Gobierno, Universidad del Desarrollo,Santiago, Chile
  8. Departamento de Neurocirugía, Facultad de Medicina, Pontificia Universidad Católica de Chile,Santiago, Chile
  9. Departamento de Psiquiatría, Facultad de Medicina, Pontificia Universidad Católica de Chile,Santiago, Chile
  10. Laboratorio de Neuro Circuitos, Facultad de Medicina, Pontificia Universidad Católica de Chile,Santiago, Chile
Institutions: Pontificia Universidad Católica de Chile (Chile); Finis Terrae University (Chile); Universidad del Desarrollo (Chile)
Journal: Scientific reports, volume 16, issue 1, article 18885
Dates: received 3 July 2025; accepted 17 April 2026; published online 24 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-49900-6 · PMID 42031873 · PMCID PMC13276037 · OpenAlex W7155519086
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: intracranial EEG (iEEG / ECoG / SEEG) (modality), human (organism), cognitive (subfield)
Methods: Preprocessing, Statistics, Spectral & time-frequency, Connectivity, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Neuroscience, Psychology
MeSH: Attention*, Brain*, Adult, Electrocorticography, Female, Humans, Magnetic Resonance Imaging, Male, Theta Rhythm, Young Adult (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Beca Doctorado Nacional ANID (21191510); Fondo Nacional de Desarrollo Científico y Tecnológico (1251906, 1251073)
Citations: not cited yet (Europe PMC); 92 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 4 matches between paragraphs and lines of code.

neurocics/LAN_current

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: afb0611ca27ae004a976f898c29d6dfca12ece4e, 15 June 2026
Languages: MATLAB (783), R (19), C/C++ (4), Shell (4), C (1)
Size: 1,007 files, 811 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (rt/R/littler/examples/install.r), tests, documentation
Not found: license file, CITATION.cff, continuous integration
Tools: FieldTrip (231 files), Statistics and Machine Learning Toolbox (29 files), EEGLAB (22 files), Signal Processing Toolbox (20 files), Image Processing Toolbox (16 files), Tools for NIfTI and ANALYZE image (MATLAB) (14 files), SPM (6 files), Brainstorm (5 files), FreeSurfer (5 files), GIfTI library for MATLAB (3 files), Chronux (2 files), CircStat (1 file), ERPLAB (1 file), lme4 (1 file), Wavelet Toolbox (1 file), nlme (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
812 files
At the source: github.com/neurocics/LAN_current

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41598-026-49900-6.

Tracing map

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What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 811 scripts, each with its path and the digest of its content;
  • 4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41598-026-49900-6.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 2 keywords, 10 MeSH terms, 2 funders, 92 references.

Cite

This paper

Herrero, J., Henríquez-Ch, R., Figueroa-Vargas, A., Uribe-San Martin, R., Cantillano, C., Fuentealba, P., Mellado, P., Godoy, J., Billeke, P., & Aboitiz, F. (2026). Global neural oscillations underlie performance variability and attentional state fluctuations in humans. Scientific reports, 16(1), 18885. https://doi.org/10.1038/s41598-026-49900-6

BibTeX

@article{herrero2026global,
author = {Herrero, Joaquín and Henríquez-Ch, Rodrigo and Figueroa-Vargas, Alejandra and Uribe-San Martin, Reinaldo and Cantillano, Christian and Fuentealba, Pablo and Mellado, Patricio and Godoy, Jaime and Billeke, Pablo and Aboitiz, Francisco},
title = {{Global neural oscillations underlie performance variability and attentional state fluctuations in humans}},
journal = {Scientific reports},
year = {2026},
month = apr,
volume = {16},
number = {1},
pages = {18885},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-49900-6},
url = {https://doi.org/10.1038/s41598-026-49900-6},
pmid = {42031873},
pmcid = {PMC13276037}
}

RIS

TY - JOUR
AU - Herrero, Joaquín
AU - Henríquez-Ch, Rodrigo
AU - Figueroa-Vargas, Alejandra
AU - Uribe-San Martin, Reinaldo
AU - Cantillano, Christian
AU - Fuentealba, Pablo
AU - Mellado, Patricio
AU - Godoy, Jaime
AU - Billeke, Pablo
AU - Aboitiz, Francisco
TI - Global neural oscillations underlie performance variability and attentional state fluctuations in humans
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/04/24
VL - 16
IS - 1
SP - 18885
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-49900-6
UR - https://doi.org/10.1038/s41598-026-49900-6
LA - en
ER -

CSL-JSON

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"title": "Global neural oscillations underlie performance variability and attentional state fluctuations in humans",
"container-title": "Scientific reports",
"author": [
{
"family": "Herrero",
"given": "Joaquín"
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{
"family": "Henríquez-Ch",
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},
{
"family": "Figueroa-Vargas",
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"volume": "16",
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"DOI": "10.1038/s41598-026-49900-6",
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"ISSN": "2045-2322",
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"language": "en",
"issued": {
"date-parts": [
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24
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}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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