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Phasic modulation of attentional rhythmic sampling according to task demands.

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  1. [1] § Methods › Spectrum analysis of the envelope of SSVEP ↔ fuse.m, lines 188–270 · score 0.68 · 41.5–42.5 Hz, 43.5–44.5 Hz, 41.5 Hz, 43.5 Hz, filtered, peak
  2. [2] § Methods › Spectrum analysis of the envelope of SSVEP ↔ sep.m, lines 188–270 · score 0.68 · 41.5–42.5 Hz, 43.5–44.5 Hz, 41.5 Hz, 43.5 Hz, filtered, peak
  3. [3] § Methods › Statistical tests ↔ fuse.m, lines 434–473 · score 0.53 · circ_htest, CircStat, uniformity, phase

Paper

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The authors' code

MATLAB · 881 lines · 34 KB · no license · 2 matches

  1. %% 0. fuse条件
  2. % 42 hz总是在左边，44 hz总是在右边。
  3. % trigger：10代表左边先开始闪，20代表右边先开始闪。
  4. % PO3:45; PO4:46
  6. %% 1. SSVEP topography，左右半球各选一个电极,先计算ERP，再做fft
  7. load("E:\Data\Kongqing\Exp1\fuse\fuse.mat");
  8. cfg = [];
  9. cfg.demean = 'yes';
  10. cfg.baselinewindow = [-0.2, 0];
  11. for i=1:length(eegdata)
  12. eegdata{1,i} = ft_preprocessing(cfg, eegdata{1,i});
  13. end
  15. % 分开左边先闪和右边先闪，各自计算erp
  16. subj=1:21; % 被试
  17. cfg = [];
  18. for i = 1:length(subj)
  19. cfg.trials = find(eegdata{1, subj(i)}.trialinfo == 10);
  20. erp_l{1, i} = ft_timelockanalysis(cfg,eegdata{1, subj(i)});
  21. cfg.trials = find(eegdata{1, subj(i)}.trialinfo == 20);
  22. erp_r{1, i} = ft_timelockanalysis(cfg,eegdata{1, subj(i)});
  23. i
  24. end
  26. % grandaverage
  27. cfg = [];
  28. erp_l_ave = ft_timelockgrandaverage(cfg,erp_l{1,1},erp_l{1,2},erp_l{1,3},erp_l{1,4},erp_l{1,5},erp_l{1,6},...
  29. erp_l{1,7},erp_l{1,8},erp_l{1,9},erp_l{1,10},erp_l{1,11},erp_l{1,12},erp_l{1,13},erp_l{1,14},erp_l{1,15},...
  30. erp_l{1,16},erp_l{1,17},erp_l{1,18},erp_l{1,19},erp_l{1,20},erp_l{1,21});
  31. erp_r_ave = ft_timelockgrandaverage(cfg,erp_r{1,1},erp_r{1,2},erp_r{1,3},erp_r{1,4},erp_r{1,5},erp_r{1,6},...
  32. erp_r{1,7},erp_r{1,8},erp_r{1,9},erp_r{1,10},erp_r{1,11},erp_r{1,12},erp_r{1,13},erp_r{1,14},erp_r{1,15},...
  33. erp_r{1,16},erp_r{1,17},erp_r{1,18},erp_r{1,19},erp_r{1,20},erp_r{1,21});
  34. % plot
  35. cfg = [];
  36. cfg.showlabels = 'yes';
  37. cfg.fontsize = 6;
  38. cfg.layout = 'easycapM1.mat';
  39. % cfg.ylim = [-3e-13 3e-13];
  40. ft_multiplotER(cfg, erp_l{1, 2}, erp_r{1, 2});
  41. % ft_multiplotER(cfg, erp_l_ave, erp_r_ave);
  43. cfg = [];
  44. cfg.xlim = [-0.2 1.7];
  45. % cfg.ylim = [-1e-13 3e-13];
  46. cfg.channel = 'PO3';
  47. ft_singleplotER(cfg, erp_l_ave, erp_r_ave);
  49. cfg = [];
  50. cfg.xlim = [-0.2 1.7];
  51. % cfg.ylim = [-1e-13 3e-13];
  52. cfg.channel = 'PO4';
  53. ft_singleplotER(cfg, erp_l_ave, erp_r_ave);
  55. % 导出原始数据
  56. po3_erp_l=erp_l_ave.avg(45,:);
  57. po3_erp_r=erp_r_ave.avg(45,:);
  58. po4_erp_l=erp_l_ave.avg(46,:);
  59. po4_erp_r=erp_r_ave.avg(46,:);
  60. t=erp_l_ave.time;
  61. save('erp_fuse.mat','po3_erp_l','po3_erp_r','po4_erp_l','po4_erp_r','t');
  63. % 分开基线前和基线后
  64. cfg = [];
  65. cfg.toilim = [-0.2, 0];
  66. for i=1:length(eegdata)
  67. eegdata_bs{1,i} = ft_redefinetrial(cfg, eegdata{1,i});
  68. end
  69. cfg = [];
  70. cfg.toilim = [0, 1.7];
  71. for i=1:length(eegdata)
  72. eegdata_er{1,i} = ft_redefinetrial(cfg, eegdata{1,i});
  73. end
  75. % 分开左边先闪和右边先闪，各自计算erp
  76. subj=1:21;
  77. cfg = [];
  78. for i = 1:length(subj)
  79. cfg.trials = find(eegdata_bs{1, subj(i)}.trialinfo == 10);
  80. erp_bs_l{1, i} = ft_timelockanalysis(cfg,eegdata_bs{1, subj(i)});
  81. cfg.trials = find(eegdata_bs{1, subj(i)}.trialinfo == 20);
  82. erp_bs_r{1, i} = ft_timelockanalysis(cfg,eegdata_bs{1, subj(i)});
  83. cfg.trials = find(eegdata_er{1, subj(i)}.trialinfo == 10);
  84. erp_er_l{1, i} = ft_timelockanalysis(cfg,eegdata_er{1, subj(i)});
  85. cfg.trials = find(eegdata_er{1, subj(i)}.trialinfo == 20);
  86. erp_er_r{1, i} = ft_timelockanalysis(cfg,eegdata_er{1, subj(i)});
  87. i
  88. end
  90. % FFT analysis
  91. cfg = [];
  92. cfg.output = 'pow';
  93. cfg.channel = 'all';
  94. cfg.method = 'mtmfft';
  95. cfg.pad = 10;
  96. cfg.foi = 36:1:48;
  97. cfg.taper = 'hanning';
  98. for i=1:length(subj)
  99. erp_bs_l_fft{1, i} = ft_freqanalysis(cfg, erp_bs_l{1, i});
  100. erp_bs_r_fft{1, i} = ft_freqanalysis(cfg, erp_bs_r{1, i});
  101. erp_er_l_fft{1, i} = ft_freqanalysis(cfg, erp_er_l{1, i});
  102. erp_er_r_fft{1, i} = ft_freqanalysis(cfg, erp_er_r{1, i});
  103. i
  104. end
  106. % convert to zscore
  107. erp_er_l_fft_z=erp_er_l_fft;
  108. erp_er_r_fft_z=erp_er_r_fft;
  109. for i = 1:length(subj)
  110. erp_er_l_fft_z{1, i}.powspctrm = zscore(erp_er_l_fft{1, i}.powspctrm,0,2);
  111. erp_er_r_fft_z{1, i}.powspctrm = zscore(erp_er_r_fft{1, i}.powspctrm,0,2);
  112. end
  114. erp_er_l_fft_z_ave = erp_er_l_fft_z{1, 1};
  115. erp_er_r_fft_z_ave = erp_er_r_fft_z{1, 1};
  116. spct_er_l = 0;
  117. spct_er_r = 0;
  118. for i = 1:length(subj)
  119. spct_er_l = spct_er_l + erp_er_l_fft_z{1, i}.powspctrm;
  120. spct_er_r = spct_er_r + erp_er_r_fft_z{1, i}.powspctrm;
  121. end
  122. erp_er_l_fft_z_ave.powspctrm = spct_er_l/length(subj);
  123. erp_er_r_fft_z_ave.powspctrm = spct_er_r/length(subj);
  125. % plot
  126. cfg = [];
  127. cfg.parameter = 'powspctrm';
  128. cfg.showlabels = 'yes';
  129. cfg.layout = 'easycapM1.mat';
  130. figure;
  131. ft_multiplotER(cfg, erp_er_l_fft_z_ave);
  133. figure;
  134. ft_multiplotER(cfg, erp_er_r_fft_z_ave);
  136. % topo plot
  137. cfg = [];
  138. cfg.parameter = 'powspctrm';
  139. cfg.xlim = [42, 42];
  140. cfg.zlim = [-0.4, 2.4];
  141. cfg.colormap = brewermap([],'*RdBu');
  142. cfg.colorbar = 'yes';
  143. cfg.layout = 'easycapM1.mat';
  145. figure;
  146. ft_topoplotER(cfg, erp_er_l_fft_z_ave);
  147. figure;
  148. ft_topoplotER(cfg, erp_er_r_fft_z_ave);
  150. cfg.xlim = [44, 44];
  151. cfg.zlim = [-0.4, 2];
  152. figure;
  153. ft_topoplotER(cfg, erp_er_l_fft_z_ave);
  154. figure;
  155. ft_topoplotER(cfg, erp_er_r_fft_z_ave);
  157. % 导出频谱
  158. for i = 1:length(subj)
  159. spec_po3_l(i,:)=erp_er_l_fft_z{1, i}.powspctrm(45,:);
  160. spec_po3_r(i,:)=erp_er_r_fft_z{1, i}.powspctrm(45,:);
  161. spec_po4_l(i,:)=erp_er_l_fft_z{1, i}.powspctrm(46,:);
  162. spec_po4_r(i,:)=erp_er_r_fft_z{1, i}.powspctrm(46,:);
  163. end
  164. f=erp_er_l_fft_z_ave.freq;
  165. save('spect_fuse.mat','spec_po3_l','spec_po3_r','spec_po4_l','spec_po4_r','f');
  167. % 提取PO3和PO4的数值来统计
  168. % 44 hz
  169. for i = 1:length(subj)
  170. fft_po3_bs_l(i)=erp_bs_l_fft{1, i}.powspctrm(45,9);
  171. fft_po3_bs_r(i)=erp_bs_r_fft{1, i}.powspctrm(45,9);
  172. fft_po3_er_l(i)=erp_er_l_fft{1, i}.powspctrm(45,9);
  173. fft_po3_er_r(i)=erp_er_r_fft{1, i}.powspctrm(45,9);
  174. end
  175. fft_po3_44_bs=(fft_po3_bs_l+fft_po3_bs_r)/2;
  176. fft_po3_44_er=(fft_po3_er_l+fft_po3_er_r)/2;
  177. % 42 hz
  178. for i = 1:length(subj)
  179. fft_po4_bs_l(i)=erp_bs_l_fft{1, i}.powspctrm(46,7);
  180. fft_po4_bs_r(i)=erp_bs_r_fft{1, i}.powspctrm(46,7);
  181. fft_po4_er_l(i)=erp_er_l_fft{1, i}.powspctrm(46,7);
  182. fft_po4_er_r(i)=erp_er_r_fft{1, i}.powspctrm(46,7);
  183. end
  184. fft_po4_42_bs=(fft_po4_bs_l+fft_po4_bs_r)/2;
  185. fft_po4_42_er=(fft_po4_er_l+fft_po4_er_r)/2;
  186. save('spect_value_fuse.mat','fft_po3_44_bs','fft_po3_44_er','fft_po4_42_bs','fft_po4_42_er');
  188. %% 2. 抽取选择的电极上SSVEP的amplitude envelop
  189. subj=1:21;
  190. % filter
  191. cfg = [];
  192. cfg.bpfilter = 'yes';
  193. for i = 1:length(subj)
  194. cfg.bpfreq = [41.5, 42.5];
  195. cfg.trials = find(eegdata_er{1, subj(i)}.trialinfo == 10);
  196. eeg_42_l{1, i} = ft_preprocessing(cfg,eegdata_er{1, subj(i)});
  197. cfg.trials = find(eegdata_er{1, subj(i)}.trialinfo == 20);
  198. eeg_42_r{1, i} = ft_preprocessing(cfg,eegdata_er{1, subj(i)});
  199. i
  200. end
  201. for i = 1:length(subj)
  202. cfg.bpfreq = [43.5, 44.5];
  203. cfg.trials = find(eegdata_er{1, subj(i)}.trialinfo == 10);
  204. eeg_44_l{1, i} = ft_preprocessing(cfg,eegdata_er{1, subj(i)});
  205. cfg.trials = find(eegdata_er{1, subj(i)}.trialinfo == 20);
  206. eeg_44_r{1, i} = ft_preprocessing(cfg,eegdata_er{1, subj(i)});
  207. i
  208. end
  210. % envelope
  211. for i = 1:length(subj)
  212. for j = 1:160
  213. for m = 1:65
  214. [u1,l1]=envelope(eeg_42_l{1, i}.trial{1, j}(m,:),13,'peak');
  215. eeg_42_l{1, i}.trial{1, j}(m,:)=u1;
  216. [u2,l2]=envelope(eeg_42_r{1, i}.trial{1, j}(m,:),13,'peak');
  217. eeg_42_r{1, i}.trial{1, j}(m,:)=u2;
  218. [u3,l3]=envelope(eeg_44_l{1, i}.trial{1, j}(m,:),13,'peak');
  219. eeg_44_l{1, i}.trial{1, j}(m,:)=u3;
  220. [u4,l4]=envelope(eeg_44_r{1, i}.trial{1, j}(m,:),13,'peak');
  221. eeg_44_r{1, i}.trial{1, j}(m,:)=u4;
  222. end
  223. end
  224. i
  225. end
  227. % time-lock analysis
  228. cfg = [];
  229. for i = 1:length(subj)
  230. hilbert_42_l{1, i} = ft_timelockanalysis(cfg,eeg_42_l{1, i});
  231. hilbert_42_r{1, i} = ft_timelockanalysis(cfg,eeg_42_r{1, i});
  232. hilbert_44_l{1, i} = ft_timelockanalysis(cfg,eeg_44_l{1, i});
  233. hilbert_44_r{1, i} = ft_timelockanalysis(cfg,eeg_44_r{1, i});
  234. i
  235. end
  237. % grandaverage
  238. cfg = [];
  239. hilbert_42_l_ave = ft_timelockgrandaverage(cfg,hilbert_42_l{1,1},hilbert_42_l{1,2},hilbert_42_l{1,3},hilbert_42_l{1,4},hilbert_42_l{1,5},hilbert_42_l{1,6},...
  240. hilbert_42_l{1,7},hilbert_42_l{1,8},hilbert_42_l{1,9},hilbert_42_l{1,10},hilbert_42_l{1,11},hilbert_42_l{1,12},hilbert_42_l{1,13},hilbert_42_l{1,14},hilbert_42_l{1,15},...
  241. hilbert_42_l{1,16},hilbert_42_l{1,17},hilbert_42_l{1,18},hilbert_42_l{1,19},hilbert_42_l{1,20},hilbert_42_l{1,21});
  242. hilbert_42_r_ave = ft_timelockgrandaverage(cfg,hilbert_42_r{1,1},hilbert_42_r{1,2},hilbert_42_r{1,3},hilbert_42_r{1,4},hilbert_42_r{1,5},hilbert_42_r{1,6},...
  243. hilbert_42_r{1,7},hilbert_42_r{1,8},hilbert_42_r{1,9},hilbert_42_r{1,10},hilbert_42_r{1,11},hilbert_42_r{1,12},hilbert_42_r{1,13},hilbert_42_r{1,14},hilbert_42_r{1,15},...
  244. hilbert_42_r{1,16},hilbert_42_r{1,17},hilbert_42_r{1,18},hilbert_42_r{1,19},hilbert_42_r{1,20},hilbert_42_r{1,21});
  245. hilbert_44_l_ave = ft_timelockgrandaverage(cfg,hilbert_44_l{1,1},hilbert_44_l{1,2},hilbert_44_l{1,3},hilbert_44_l{1,4},hilbert_44_l{1,5},hilbert_44_l{1,6},...
  246. hilbert_44_l{1,7},hilbert_44_l{1,8},hilbert_44_l{1,9},hilbert_44_l{1,10},hilbert_44_l{1,11},hilbert_44_l{1,12},hilbert_44_l{1,13},hilbert_44_l{1,14},hilbert_44_l{1,15},...
  247. hilbert_44_l{1,16},hilbert_44_l{1,17},hilbert_44_l{1,18},hilbert_44_l{1,19},hilbert_44_l{1,20},hilbert_44_l{1,21});
  248. hilbert_44_r_ave = ft_timelockgrandaverage(cfg,hilbert_44_r{1,1},hilbert_44_r{1,2},hilbert_44_r{1,3},hilbert_44_r{1,4},hilbert_44_r{1,5},hilbert_44_r{1,6},...
  249. hilbert_44_r{1,7},hilbert_44_r{1,8},hilbert_44_r{1,9},hilbert_44_r{1,10},hilbert_44_r{1,11},hilbert_44_r{1,12},hilbert_44_r{1,13},hilbert_44_r{1,14},hilbert_44_r{1,15},...
  250. hilbert_44_r{1,16},hilbert_44_r{1,17},hilbert_44_r{1,18},hilbert_44_r{1,19},hilbert_44_r{1,20},hilbert_44_r{1,21});
  252. % plot
  253. cfg = [];
  254. cfg.showlabels = 'yes';
  255. cfg.fontsize = 6;
  256. cfg.layout = 'easycapM1.mat';
  257. % cfg.ylim = [-3e-13 3e-13];
  258. ft_multiplotER(cfg, hilbert_42_l{1, 1}, hilbert_42_r{1, 1});
  260. cfg = [];
  261. cfg.xlim = [0 1.7];
  262. % cfg.ylim = [-1e-13 3e-13];
  263. cfg.channel = 'PO3';
  264. ft_singleplotER(cfg, hilbert_42_l_ave, hilbert_42_r_ave);
  266. cfg = [];
  267. cfg.xlim = [0 1.7];
  268. % cfg.ylim = [-1e-13 3e-13];
  269. cfg.channel = 'PO4';
  270. ft_singleplotER(cfg, hilbert_42_l_ave, hilbert_42_r_ave);
  272. %% 3. 两个条件的envelop之间的关系，考察它们之间的相位差
  273. % FFT analysis
  274. cfg = [];
  275. cfg.output = 'fooof_peaks';
  276. cfg.channel = 'all';
  277. cfg.method = 'mtmfft';
  278. cfg.pad = 10;
  279. cfg.foi = 2:0.2:10;
  280. cfg.taper = 'hanning';
  281. for i=1:length(subj)
  282. eeg_42_l_fft{1, i} = ft_freqanalysis(cfg, eeg_42_l{1, i});
  283. eeg_42_r_fft{1, i} = ft_freqanalysis(cfg, eeg_42_r{1, i});
  284. eeg_44_l_fft{1, i} = ft_freqanalysis(cfg, eeg_44_l{1, i});
  285. eeg_44_r_fft{1, i} = ft_freqanalysis(cfg, eeg_44_r{1, i});
  286. i
  287. end
  288. freq=2:0.2:10;
  289. % grand average
  290. eeg_42_l_fft_ave = eeg_42_l_fft{1, 1};
  291. eeg_42_r_fft_ave = eeg_42_r_fft{1, 1};
  292. eeg_44_l_fft_ave = eeg_44_l_fft{1, 1};
  293. eeg_44_r_fft_ave = eeg_44_r_fft{1, 1};
  294. for i = 1:length(subj)
  295. spct_42_l(i,:,:) = eeg_42_l_fft{1, i}.powspctrm;
  296. spct_42_r(i,:,:) = eeg_42_r_fft{1, i}.powspctrm;
  297. spct_44_l(i,:,:) = eeg_44_l_fft{1, i}.powspctrm;
  298. spct_44_r(i,:,:) = eeg_44_r_fft{1, i}.powspctrm;
  299. end
  301. eeg_42_l_fft_ave.powspctrm = squeeze(mean(spct_42_l));
  302. eeg_42_r_fft_ave.powspctrm = squeeze(mean(spct_42_r));
  303. eeg_44_l_fft_ave.powspctrm = squeeze(mean(spct_44_l));
  304. eeg_44_r_fft_ave.powspctrm = squeeze(mean(spct_44_r));
  306. % plot
  307. cfg = [];
  308. cfg.parameter = 'powspctrm';
  309. cfg.showlabels = 'yes';
  310. cfg.layout = 'easycapM1.mat';
  311. figure;
  312. ft_multiplotER(cfg, eeg_42_l_fft_ave, eeg_42_r_fft_ave);
  314. figure;
  315. ft_multiplotER(cfg, eeg_44_l_fft_ave, eeg_44_r_fft_ave);
  317. % topo plot
  318. eeg_42_l_fft_temp=eeg_42_l_fft_ave;
  319. eeg_42_l_fft_temp.powspctrm=(eeg_42_l_fft_ave.powspctrm+eeg_42_r_fft_ave.powspctrm)/2;
  320. a=eeg_42_l_fft_temp.powspctrm(47, :);
  321. eeg_42_l_fft_temp.powspctrm(47, :)=eeg_42_l_fft_temp.powspctrm(6, :);
  322. eeg_42_l_fft_temp.powspctrm(6, :)=a;
  324. eeg_44_l_fft_temp=eeg_44_l_fft_ave;
  325. eeg_44_l_fft_temp.powspctrm=(eeg_44_l_fft_ave.powspctrm+eeg_44_r_fft_ave.powspctrm)/2;
  326. b=eeg_44_l_fft_temp.powspctrm(12, :);
  327. eeg_44_l_fft_temp.powspctrm(12, :)=eeg_44_l_fft_temp.powspctrm(9, :);
  328. eeg_44_l_fft_temp.powspctrm(9, :)=b;
  329. c=eeg_44_l_fft_temp.powspctrm(48, :);
  330. eeg_44_l_fft_temp.powspctrm(48, :)=eeg_44_l_fft_temp.powspctrm(45, :);
  331. eeg_44_l_fft_temp.powspctrm(45, :)=c;
  333. cfg = [];
  334. cfg.parameter = 'powspctrm';
  335. cfg.xlim = [4.4, 4.4];
  336. cfg.zlim = [6.4, 7.4];
  337. cfg.colormap = brewermap([],'Reds');
  338. cfg.colorbar = 'yes';
  339. cfg.layout = 'easycapM1.mat';
  341. ft_topoplotER(cfg, eeg_42_l_fft_temp);
  343. cfg.xlim = [6.2, 6.2];
  344. cfg.zlim = [12.5, 14];
  345. ft_topoplotER(cfg, eeg_44_l_fft_temp);
  347. % permutation test
  348. % FFT analysis
  349. cfg = [];
  350. cfg.output = 'fooof_peaks';
  351. cfg.channel = 'all';
  352. cfg.method = 'mtmfft';
  353. cfg.pad = 10;
  354. cfg.foi = 2:0.2:10;
  355. cfg.taper = 'hanning';
  357. eeg_42_l_temp=eeg_42_l;
  358. eeg_42_r_temp=eeg_42_r;
  359. eeg_44_l_temp=eeg_44_l;
  360. eeg_44_r_temp=eeg_44_r;
  361. parob=parpool;
  362. for n=1:1000
  363. % shuffle
  364. parfor i=1:length(subj)
  365. for j=1:160
  366. for m=1:65
  367. eeg_42_l_temp{1, i}.trial{1, j}(m,:)=eeg_42_l{1, i}.trial{1, j}(m,randperm(850));
  368. eeg_42_r_temp{1, i}.trial{1, j}(m,:)=eeg_42_r{1, i}.trial{1, j}(m,randperm(850));
  369. eeg_44_l_temp{1, i}.trial{1, j}(m,:)=eeg_44_l{1, i}.trial{1, j}(m,randperm(850));
  370. eeg_44_r_temp{1, i}.trial{1, j}(m,:)=eeg_44_r{1, i}.trial{1, j}(m,randperm(850));
  371. end
  372. end
  373. eeg_42_l_fft_temp{1, i} = ft_freqanalysis(cfg, eeg_42_l_temp{1, i});
  374. eeg_42_r_fft_temp{1, i} = ft_freqanalysis(cfg, eeg_42_r_temp{1, i});
  375. eeg_44_l_fft_temp{1, i} = ft_freqanalysis(cfg, eeg_44_l_temp{1, i});
  376. eeg_44_r_fft_temp{1, i} = ft_freqanalysis(cfg, eeg_44_r_temp{1, i});
  377. end
  379. % grand average
  380. spct_42_l = 0;
  381. spct_42_r = 0;
  382. spct_44_l = 0;
  383. spct_44_r = 0;
  384. for i = 1:length(subj)
  385. spct_42_l = spct_42_l + eeg_42_l_fft_temp{1, i}.powspctrm;
  386. spct_42_r = spct_42_r + eeg_42_r_fft_temp{1, i}.powspctrm;
  387. spct_44_l = spct_44_l + eeg_44_l_fft_temp{1, i}.powspctrm;
  388. spct_44_r = spct_44_r + eeg_44_r_fft_temp{1, i}.powspctrm;
  389. end
  391. eeg_42_l_shuf(n,:,:) = spct_42_l/length(subj);
  392. eeg_42_r_shuf(n,:,:) = spct_42_r/length(subj);
  393. eeg_44_l_shuf(n,:,:) = spct_44_l/length(subj);
  394. eeg_44_r_shuf(n,:,:) = spct_44_r/length(subj);
  395. n
  396. end
  397. delete(parob);
  398. save('env_fft_shuf_fuse.mat','eeg_42_l_shuf','eeg_42_r_shuf','eeg_44_l_shuf','eeg_44_r_shuf');
  400. % 取95%的阈限
  401. load('env_fft_shuf_fuse.mat');
  402. eeg_42_l_thre = squeeze(prctile(eeg_42_l_shuf,95,1));
  403. eeg_42_r_thre = squeeze(prctile(eeg_42_r_shuf,95,1));
  404. eeg_44_l_thre = squeeze(prctile(eeg_44_l_shuf,95,1));
  405. eeg_44_r_thre = squeeze(prctile(eeg_44_r_shuf,95,1));
  407. % phase analysis
  408. cfg = [];
  409. cfg.output = 'fourier';
  410. cfg.channel = 'all';
  411. cfg.method = 'mtmfft';
  412. cfg.pad = 10;
  413. cfg.foi = 2:0.2:10;
  414. cfg.taper = 'hanning';
  415. for i=1:length(subj)
  416. eeg_42_l_phase{1, i} = ft_freqanalysis(cfg, eeg_42_l{1, i});
  417. eeg_42_r_phase{1, i} = ft_freqanalysis(cfg, eeg_42_r{1, i});
  418. eeg_44_l_phase{1, i} = ft_freqanalysis(cfg, eeg_44_l{1, i});
  419. eeg_44_r_phase{1, i} = ft_freqanalysis(cfg, eeg_44_r{1, i});
  420. i
  421. end
  423. for i=1:length(subj)
  424. po4_42_l_phase(i) = circ_mean(angle(eeg_42_l_phase{1, i}.fourierspctrm(:,46,17)));
  425. po4_42_r_phase(i) = circ_mean(angle(eeg_42_r_phase{1, i}.fourierspctrm(:,46,17)));
  426. po3_44_l_phase(i) = circ_mean(angle(eeg_44_l_phase{1, i}.fourierspctrm(:,45,17)));
  427. po3_44_r_phase(i) = circ_mean(angle(eeg_44_r_phase{1, i}.fourierspctrm(:,45,17)));
  428. i
  429. end
  431. phase_l_diff = circ_dist(po4_42_l_phase, po3_44_l_phase);
  432. phase_r_diff = circ_dist(po3_44_r_phase, po4_42_r_phase);
  434. % plot
  435. figure;
  436. subplot(131);
  437. circ_plot(po4_42_l_phase','hist',[],12,false,true,'linewidth',2,'color','r');
  438. subplot(132);
  439. circ_plot(po3_44_l_phase','hist',[],12,false,true,'linewidth',2,'color','r');
  440. subplot(133);
  441. circ_plot(phase_l_diff','hist',[],12,false,true,'linewidth',2,'color','r');
  443. circ_rad2ang(circ_mean(po4_42_l_phase,[],2));
  444. [pval z] = circ_rtest(po4_42_l_phase); % non-uniform
  445. circ_rad2ang(circ_mean(po3_44_l_phase,[],2));
  446. [pval z] = circ_rtest(po3_44_l_phase); % non-uniform
  447. circ_rad2ang(circ_mean(phase_l_diff,[],2));
  448. [pval z] = circ_rtest(phase_l_diff); % non-uniform
  450. figure;
  451. subplot(131);
  452. circ_plot(po3_44_r_phase','hist',[],12,false,true,'linewidth',2,'color','r');
  453. subplot(132);
  454. circ_plot(po4_42_r_phase','hist',[],12,false,true,'linewidth',2,'color','r');
  455. subplot(133);
  456. circ_plot(phase_r_diff','hist',[],12,false,true,'linewidth',2,'color','r');
  458. circ_rad2ang(circ_mean(po3_44_r_phase,[],2));
  459. [pval z] = circ_rtest(po3_44_r_phase); % non-uniform
  460. circ_rad2ang(circ_mean(po4_42_r_phase,[],2));
  461. [pval z] = circ_rtest(po4_42_r_phase); % non-uniform
  462. circ_rad2ang(circ_mean(phase_r_diff,[],2));
  463. [pval z] = circ_rtest(phase_r_diff); % non-uniform
  465. [pval z] = circ_vtest(phase_l_diff,circ_ang2rad(0)); % difference to 0
  466. [pval z] = circ_vtest(phase_r_diff,circ_ang2rad(0)); % difference to 0
  468. phase_diff_fuse=circ_mean([phase_l_diff; phase_r_diff],[],1);
  469. save('phase_diff_fuse.mat','phase_diff_fuse');
  471. circ_rad2ang(circ_mean(circ_dist(phase_diff_single, phase_diff_fuse),[],2));
  472. [pval, F] = circ_htest(phase_diff_single, phase_diff_fuse);
  473. circ_plot(circ_dist(phase_diff_single, phase_diff_fuse)','hist',[],12,false,true,'linewidth',2,'color','r');
  475. %% 4. Phase-amplitude coupling, SSVEP的amplitude与某个地方脑电的phase有coupling。表现为SSVEP envelope与脑电phase的coherence
  476. % 将提取EEG的频谱，然后跟ssvep的envelope计算coherence
  477. % FFT on eeg
  478. cfg = [];
  479. cfg.output = 'fourier';
  480. cfg.channel = 'all';
  481. cfg.method = 'mtmfft';
  482. cfg.pad = 10;
  483. cfg.foi = 2:0.2:10;
  484. cfg.taper = 'hanning';
  485. for i=1:length(subj)
  486. cfg.trials = find(eegdata_er{1, subj(i)}.trialinfo == 10);
  487. eeg_fft_l{1, i} = ft_freqanalysis(cfg, eegdata_er{1, subj(i)});
  488. cfg.trials = find(eegdata_er{1, subj(i)}.trialinfo == 20);
  489. eeg_fft_r{1, i} = ft_freqanalysis(cfg, eegdata_er{1, subj(i)});
  490. i
  491. end
  493. % 选择电极，替换IO。计算IO与其他电极的coherence
  494. % 左边先开始闪，PO4（46）替换
  495. for i=1:length(subj)
  496. eeg_fft_l{1, i}.fourierspctrm(:,20,:)=eeg_42_l_phase{1, i}.fourierspctrm(:,46,:);
  497. end
  499. cfg = [];
  500. cfg.method = 'coh';
  501. cfg.channelcmb = {'IO' 'Fp1'; 'IO' 'Fp2'; 'IO' 'F3'; 'IO' 'F4'; 'IO' 'C3'; 'IO' 'C4';...
  502. 'IO' 'P3'; 'IO' 'P4'; 'IO' 'O1'; 'IO' 'O2'; 'IO' 'F7'; 'IO' 'F8'; 'IO' 'T7';...
  503. 'IO' 'T8'; 'IO' 'P7'; 'IO' 'P8'; 'IO' 'Fz'; 'IO' 'Cz'; 'IO' 'Pz'; 'IO' 'FC1';...
  504. 'IO' 'FC2'; 'IO' 'CP1'; 'IO' 'CP2'; 'IO' 'FC5'; 'IO' 'FC6'; 'IO' 'CP5'; 'IO' 'CP6';...
  505. 'IO' 'FT9'; 'IO' 'FT10'; 'IO' 'TP9';'IO' 'TP10';'IO' 'F1';'IO' 'F2';'IO' 'C1';...
  506. 'IO' 'C2';'IO' 'P1';'IO' 'P2';'IO' 'AF3';'IO' 'AF4';'IO' 'FC3';'IO' 'FC4';...
  507. 'IO' 'CP3';'IO' 'CP4';'IO' 'PO3';'IO' 'PO4';'IO' 'F5';'IO' 'F6';'IO' 'C5';...
  508. 'IO' 'C6';'IO' 'P5';'IO' 'P6';'IO' 'AF7';'IO' 'AF8';'IO' 'FT7';'IO' 'FT8';...
  509. 'IO' 'TP7';'IO' 'TP8';'IO' 'PO7';'IO' 'PO8';'IO' 'Fpz';'IO' 'CPz';'IO' 'POz';...
  510. 'IO' 'Oz';'IO' 'FCz'};
  511. for i=1:length(subj)
  512. coh_l_po4{1, i}= ft_connectivityanalysis(cfg, eeg_fft_l{1, i});
  513. end
  515. % 左边先开始闪，PO3（45）
  516. for i=1:length(subj)
  517. eeg_fft_l{1, i}.fourierspctrm(:,20,:)=eeg_44_l_phase{1, i}.fourierspctrm(:,45,:);
  518. end
  520. cfg = [];
  521. cfg.method = 'coh';
  522. cfg.channelcmb = {'IO' 'Fp1'; 'IO' 'Fp2'; 'IO' 'F3'; 'IO' 'F4'; 'IO' 'C3'; 'IO' 'C4';...
  523. 'IO' 'P3'; 'IO' 'P4'; 'IO' 'O1'; 'IO' 'O2'; 'IO' 'F7'; 'IO' 'F8'; 'IO' 'T7';...
  524. 'IO' 'T8'; 'IO' 'P7'; 'IO' 'P8'; 'IO' 'Fz'; 'IO' 'Cz'; 'IO' 'Pz'; 'IO' 'FC1';...
  525. 'IO' 'FC2'; 'IO' 'CP1'; 'IO' 'CP2'; 'IO' 'FC5'; 'IO' 'FC6'; 'IO' 'CP5'; 'IO' 'CP6';...
  526. 'IO' 'FT9'; 'IO' 'FT10'; 'IO' 'TP9';'IO' 'TP10';'IO' 'F1';'IO' 'F2';'IO' 'C1';...
  527. 'IO' 'C2';'IO' 'P1';'IO' 'P2';'IO' 'AF3';'IO' 'AF4';'IO' 'FC3';'IO' 'FC4';...
  528. 'IO' 'CP3';'IO' 'CP4';'IO' 'PO3';'IO' 'PO4';'IO' 'F5';'IO' 'F6';'IO' 'C5';...
  529. 'IO' 'C6';'IO' 'P5';'IO' 'P6';'IO' 'AF7';'IO' 'AF8';'IO' 'FT7';'IO' 'FT8';...
  530. 'IO' 'TP7';'IO' 'TP8';'IO' 'PO7';'IO' 'PO8';'IO' 'Fpz';'IO' 'CPz';'IO' 'POz';...
  531. 'IO' 'Oz';'IO' 'FCz'};
  532. for i=1:length(subj)
  533. coh_l_po3{1, i}= ft_connectivityanalysis(cfg, eeg_fft_l{1, i});
  534. end
  536. % 右边先开始闪，PO3（45）
  537. for i=1:length(subj)
  538. eeg_fft_r{1, i}.fourierspctrm(:,20,:)=eeg_44_r_phase{1, i}.fourierspctrm(:,45,:);
  539. end
  541. cfg = [];
  542. cfg.method = 'coh';
  543. cfg.channelcmb = {'IO' 'Fp1'; 'IO' 'Fp2'; 'IO' 'F3'; 'IO' 'F4'; 'IO' 'C3'; 'IO' 'C4';...
  544. 'IO' 'P3'; 'IO' 'P4'; 'IO' 'O1'; 'IO' 'O2'; 'IO' 'F7'; 'IO' 'F8'; 'IO' 'T7';...
  545. 'IO' 'T8'; 'IO' 'P7'; 'IO' 'P8'; 'IO' 'Fz'; 'IO' 'Cz'; 'IO' 'Pz'; 'IO' 'FC1';...
  546. 'IO' 'FC2'; 'IO' 'CP1'; 'IO' 'CP2'; 'IO' 'FC5'; 'IO' 'FC6'; 'IO' 'CP5'; 'IO' 'CP6';...
  547. 'IO' 'FT9'; 'IO' 'FT10'; 'IO' 'TP9';'IO' 'TP10';'IO' 'F1';'IO' 'F2';'IO' 'C1';...
  548. 'IO' 'C2';'IO' 'P1';'IO' 'P2';'IO' 'AF3';'IO' 'AF4';'IO' 'FC3';'IO' 'FC4';...
  549. 'IO' 'CP3';'IO' 'CP4';'IO' 'PO3';'IO' 'PO4';'IO' 'F5';'IO' 'F6';'IO' 'C5';...
  550. 'IO' 'C6';'IO' 'P5';'IO' 'P6';'IO' 'AF7';'IO' 'AF8';'IO' 'FT7';'IO' 'FT8';...
  551. 'IO' 'TP7';'IO' 'TP8';'IO' 'PO7';'IO' 'PO8';'IO' 'Fpz';'IO' 'CPz';'IO' 'POz';...
  552. 'IO' 'Oz';'IO' 'FCz'};
  553. for i=1:length(subj)
  554. coh_r_po3{1, i}= ft_connectivityanalysis(cfg, eeg_fft_r{1, i});
  555. end
  557. % 右边先开始闪，PO4（46）
  558. for i=1:length(subj)
  559. eeg_fft_r{1, i}.fourierspctrm(:,20,:)=eeg_42_r_phase{1, i}.fourierspctrm(:,46,:);
  560. end
  562. cfg = [];
  563. cfg.method = 'coh';
  564. cfg.channelcmb = {'IO' 'Fp1'; 'IO' 'Fp2'; 'IO' 'F3'; 'IO' 'F4'; 'IO' 'C3'; 'IO' 'C4';...
  565. 'IO' 'P3'; 'IO' 'P4'; 'IO' 'O1'; 'IO' 'O2'; 'IO' 'F7'; 'IO' 'F8'; 'IO' 'T7';...
  566. 'IO' 'T8'; 'IO' 'P7'; 'IO' 'P8'; 'IO' 'Fz'; 'IO' 'Cz'; 'IO' 'Pz'; 'IO' 'FC1';...
  567. 'IO' 'FC2'; 'IO' 'CP1'; 'IO' 'CP2'; 'IO' 'FC5'; 'IO' 'FC6'; 'IO' 'CP5'; 'IO' 'CP6';...
  568. 'IO' 'FT9'; 'IO' 'FT10'; 'IO' 'TP9';'IO' 'TP10';'IO' 'F1';'IO' 'F2';'IO' 'C1';...
  569. 'IO' 'C2';'IO' 'P1';'IO' 'P2';'IO' 'AF3';'IO' 'AF4';'IO' 'FC3';'IO' 'FC4';...
  570. 'IO' 'CP3';'IO' 'CP4';'IO' 'PO3';'IO' 'PO4';'IO' 'F5';'IO' 'F6';'IO' 'C5';...
  571. 'IO' 'C6';'IO' 'P5';'IO' 'P6';'IO' 'AF7';'IO' 'AF8';'IO' 'FT7';'IO' 'FT8';...
  572. 'IO' 'TP7';'IO' 'TP8';'IO' 'PO7';'IO' 'PO8';'IO' 'Fpz';'IO' 'CPz';'IO' 'POz';...
  573. 'IO' 'Oz';'IO' 'FCz'};
  574. for i=1:length(subj)
  575. coh_r_po4{1, i}= ft_connectivityanalysis(cfg, eeg_fft_r{1, i});
  576. end
  578. % the coh value of 4 hz
  579. for i=1:length(subj)
  580. coh_l_po4_val(:,i)=coh_l_po4{1, i}.cohspctrm(:,13);
  581. coh_l_po3_val(:,i)=coh_l_po3{1, i}.cohspctrm(:,22);
  582. coh_r_po4_val(:,i)=coh_r_po4{1, i}.cohspctrm(:,13);
  583. coh_r_po3_val(:,i)=coh_r_po3{1, i}.cohspctrm(:,22);
  584. end
  586. % topo plot
  587. chann_label=eegdata{1, 1}.label;
  588. chann_label(20)=[];
  590. cfg = [];
  591. cfg.layout = 'easycapM1.mat';
  592. cfg.xlim = 'maxmin'; % time limitation
  593. % cfg.zlim = [0.5 1];
  594. % cfg.style = 'straight';
  595. cfg.colorbar = 'yes';
  596. cfg.marker = 'on';
  597. cfg.comment = 'xlim';
  598. cfg.commentpos = 'lefttop';
  600. coh_l_po4_plot = {};
  601. coh_l_po4_plot.avg = mean(coh_l_po4_val,2); % data definition, channel * time
  602. coh_l_po4_plot.time = 1; % time definition
  603. coh_l_po4_plot.dimord = 'chan_time';
  604. coh_l_po4_plot.label = chann_label;
  606. figure;
  607. ft_topoplotER(cfg, coh_l_po4_plot);
  608. title('coh_l_po4');
  609. axis tight;
  611. coh_l_po3_plot = {};
  612. coh_l_po3_plot.avg = mean(coh_l_po3_val,2); % data definition, channel * time
  613. coh_l_po3_plot.time = 1; % time definition
  614. coh_l_po3_plot.dimord = 'chan_time';
  615. coh_l_po3_plot.label = chann_label;
  617. figure;
  618. ft_topoplotER(cfg, coh_l_po3_plot);
  619. title('coh_l_po3');
  620. axis tight;
  622. coh_r_po3_plot = {};
  623. coh_r_po3_plot.avg = mean(coh_r_po3_val,2); % data definition, channel * time
  624. coh_r_po3_plot.time = 1; % time definition
  625. coh_r_po3_plot.dimord = 'chan_time';
  626. coh_r_po3_plot.label = chann_label;
  628. figure;
  629. ft_topoplotER(cfg, coh_r_po3_plot);
  630. title('coh_r_po3');
  631. axis tight;
  633. coh_r_po4_plot = {};
  634. coh_r_po4_plot.avg = mean(coh_r_po4_val,2); % data definition, channel * time
  635. coh_r_po4_plot.time = 1; % time definition
  636. coh_r_po4_plot.dimord = 'chan_time';
  637. coh_r_po4_plot.label = chann_label;
  639. figure;
  640. ft_topoplotER(cfg, coh_r_po4_plot);
  641. title('coh_r_po3');
  642. axis tight;
  644. % permutation test
  645. eegdata_er_sel=eegdata_er(subj);
  646. parob=parpool;
  647. for n=1:1000
  648. % shuffle
  649. eegdata_er_temp=eegdata_er_sel;
  650. parfor i=1:length(subj)
  651. for j=1:320
  652. for m=1:65
  653. eegdata_er_temp{1, i}.trial{1, j}(m,:)=eegdata_er_sel{1, i}.trial{1, j}(m,randperm(850));
  654. end
  655. end
  656. end
  658. % FFT on eeg
  659. cfg = [];
  660. cfg.output = 'fourier';
  661. cfg.channel = 'all';
  662. cfg.method = 'mtmfft';
  663. cfg.pad = 10;
  664. cfg.foi = 2:0.2:10;
  665. cfg.taper = 'hanning';
  666. for i=1:length(subj)
  667. cfg.trials = find(eegdata_er_temp{1, i}.trialinfo == 10);
  668. eeg_fft_l_temp{1, i} = ft_freqanalysis(cfg, eegdata_er_temp{1, i});
  669. cfg.trials = find(eegdata_er_temp{1, i}.trialinfo == 20);
  670. eeg_fft_r_temp{1, i} = ft_freqanalysis(cfg, eegdata_er_temp{1, i});
  671. end
  673. % 选择电极，替换IO。计算IO与其他电极的coherence
  674. % 左边先开始闪，PO4（46）替换
  675. for i=1:length(subj)
  676. eeg_fft_l_temp{1, i}.fourierspctrm(:,20,:)=eeg_42_l_phase{1, i}.fourierspctrm(:,46,:);
  677. end
  679. cfg = [];
  680. cfg.method = 'coh';
  681. cfg.channelcmb = {'IO' 'Fp1'; 'IO' 'Fp2'; 'IO' 'F3'; 'IO' 'F4'; 'IO' 'C3'; 'IO' 'C4';...
  682. 'IO' 'P3'; 'IO' 'P4'; 'IO' 'O1'; 'IO' 'O2'; 'IO' 'F7'; 'IO' 'F8'; 'IO' 'T7';...
  683. 'IO' 'T8'; 'IO' 'P7'; 'IO' 'P8'; 'IO' 'Fz'; 'IO' 'Cz'; 'IO' 'Pz'; 'IO' 'FC1';...
  684. 'IO' 'FC2'; 'IO' 'CP1'; 'IO' 'CP2'; 'IO' 'FC5'; 'IO' 'FC6'; 'IO' 'CP5'; 'IO' 'CP6';...
  685. 'IO' 'FT9'; 'IO' 'FT10'; 'IO' 'TP9';'IO' 'TP10';'IO' 'F1';'IO' 'F2';'IO' 'C1';...
  686. 'IO' 'C2';'IO' 'P1';'IO' 'P2';'IO' 'AF3';'IO' 'AF4';'IO' 'FC3';'IO' 'FC4';...
  687. 'IO' 'CP3';'IO' 'CP4';'IO' 'PO3';'IO' 'PO4';'IO' 'F5';'IO' 'F6';'IO' 'C5';...
  688. 'IO' 'C6';'IO' 'P5';'IO' 'P6';'IO' 'AF7';'IO' 'AF8';'IO' 'FT7';'IO' 'FT8';...
  689. 'IO' 'TP7';'IO' 'TP8';'IO' 'PO7';'IO' 'PO8';'IO' 'Fpz';'IO' 'CPz';'IO' 'POz';...
  690. 'IO' 'Oz';'IO' 'FCz'};
  691. for i=1:length(subj)
  692. coh_l_po4_temp{1, i}= ft_connectivityanalysis(cfg, eeg_fft_l_temp{1, i});
  693. coh_l_po4_temp_val(i,:,:)=coh_l_po4_temp{1, i}.cohspctrm;
  694. end
  696. % 左边先开始闪，PO3（45）
  697. for i=1:length(subj)
  698. eeg_fft_l_temp{1, i}.fourierspctrm(:,20,:)=eeg_44_l_phase{1, i}.fourierspctrm(:,45,:);
  699. end
  701. cfg = [];
  702. cfg.method = 'coh';
  703. cfg.channelcmb = {'IO' 'Fp1'; 'IO' 'Fp2'; 'IO' 'F3'; 'IO' 'F4'; 'IO' 'C3'; 'IO' 'C4';...
  704. 'IO' 'P3'; 'IO' 'P4'; 'IO' 'O1'; 'IO' 'O2'; 'IO' 'F7'; 'IO' 'F8'; 'IO' 'T7';...
  705. 'IO' 'T8'; 'IO' 'P7'; 'IO' 'P8'; 'IO' 'Fz'; 'IO' 'Cz'; 'IO' 'Pz'; 'IO' 'FC1';...
  706. 'IO' 'FC2'; 'IO' 'CP1'; 'IO' 'CP2'; 'IO' 'FC5'; 'IO' 'FC6'; 'IO' 'CP5'; 'IO' 'CP6';...
  707. 'IO' 'FT9'; 'IO' 'FT10'; 'IO' 'TP9';'IO' 'TP10';'IO' 'F1';'IO' 'F2';'IO' 'C1';...
  708. 'IO' 'C2';'IO' 'P1';'IO' 'P2';'IO' 'AF3';'IO' 'AF4';'IO' 'FC3';'IO' 'FC4';...
  709. 'IO' 'CP3';'IO' 'CP4';'IO' 'PO3';'IO' 'PO4';'IO' 'F5';'IO' 'F6';'IO' 'C5';...
  710. 'IO' 'C6';'IO' 'P5';'IO' 'P6';'IO' 'AF7';'IO' 'AF8';'IO' 'FT7';'IO' 'FT8';...
  711. 'IO' 'TP7';'IO' 'TP8';'IO' 'PO7';'IO' 'PO8';'IO' 'Fpz';'IO' 'CPz';'IO' 'POz';...
  712. 'IO' 'Oz';'IO' 'FCz'};
  713. for i=1:length(subj)
  714. coh_l_po3_temp{1, i}= ft_connectivityanalysis(cfg, eeg_fft_l_temp{1, i});
  715. coh_l_po3_temp_val(i,:,:)=coh_l_po3_temp{1, i}.cohspctrm;
  716. end
  718. % 右边先开始闪，PO3（45）
  719. for i=1:length(subj)
  720. eeg_fft_r_temp{1, i}.fourierspctrm(:,20,:)=eeg_44_r_phase{1, i}.fourierspctrm(:,45,:);
  721. end
  723. cfg = [];
  724. cfg.method = 'coh';
  725. cfg.channelcmb = {'IO' 'Fp1'; 'IO' 'Fp2'; 'IO' 'F3'; 'IO' 'F4'; 'IO' 'C3'; 'IO' 'C4';...
  726. 'IO' 'P3'; 'IO' 'P4'; 'IO' 'O1'; 'IO' 'O2'; 'IO' 'F7'; 'IO' 'F8'; 'IO' 'T7';...
  727. 'IO' 'T8'; 'IO' 'P7'; 'IO' 'P8'; 'IO' 'Fz'; 'IO' 'Cz'; 'IO' 'Pz'; 'IO' 'FC1';...
  728. 'IO' 'FC2'; 'IO' 'CP1'; 'IO' 'CP2'; 'IO' 'FC5'; 'IO' 'FC6'; 'IO' 'CP5'; 'IO' 'CP6';...
  729. 'IO' 'FT9'; 'IO' 'FT10'; 'IO' 'TP9';'IO' 'TP10';'IO' 'F1';'IO' 'F2';'IO' 'C1';...
  730. 'IO' 'C2';'IO' 'P1';'IO' 'P2';'IO' 'AF3';'IO' 'AF4';'IO' 'FC3';'IO' 'FC4';...
  731. 'IO' 'CP3';'IO' 'CP4';'IO' 'PO3';'IO' 'PO4';'IO' 'F5';'IO' 'F6';'IO' 'C5';...
  732. 'IO' 'C6';'IO' 'P5';'IO' 'P6';'IO' 'AF7';'IO' 'AF8';'IO' 'FT7';'IO' 'FT8';...
  733. 'IO' 'TP7';'IO' 'TP8';'IO' 'PO7';'IO' 'PO8';'IO' 'Fpz';'IO' 'CPz';'IO' 'POz';...
  734. 'IO' 'Oz';'IO' 'FCz'};
  735. for i=1:length(subj)
  736. coh_r_po3_temp{1, i}= ft_connectivityanalysis(cfg, eeg_fft_r_temp{1, i});
  737. coh_r_po3_temp_val(i,:,:)=coh_r_po3_temp{1, i}.cohspctrm;
  738. end
  740. % 右边先开始闪，PO4（46）
  741. for i=1:length(subj)
  742. eeg_fft_r_temp{1, i}.fourierspctrm(:,20,:)=eeg_42_r_phase{1, i}.fourierspctrm(:,46,:);
  743. end
  745. cfg = [];
  746. cfg.method = 'coh';
  747. cfg.channelcmb = {'IO' 'Fp1'; 'IO' 'Fp2'; 'IO' 'F3'; 'IO' 'F4'; 'IO' 'C3'; 'IO' 'C4';...
  748. 'IO' 'P3'; 'IO' 'P4'; 'IO' 'O1'; 'IO' 'O2'; 'IO' 'F7'; 'IO' 'F8'; 'IO' 'T7';...
  749. 'IO' 'T8'; 'IO' 'P7'; 'IO' 'P8'; 'IO' 'Fz'; 'IO' 'Cz'; 'IO' 'Pz'; 'IO' 'FC1';...
  750. 'IO' 'FC2'; 'IO' 'CP1'; 'IO' 'CP2'; 'IO' 'FC5'; 'IO' 'FC6'; 'IO' 'CP5'; 'IO' 'CP6';...
  751. 'IO' 'FT9'; 'IO' 'FT10'; 'IO' 'TP9';'IO' 'TP10';'IO' 'F1';'IO' 'F2';'IO' 'C1';...
  752. 'IO' 'C2';'IO' 'P1';'IO' 'P2';'IO' 'AF3';'IO' 'AF4';'IO' 'FC3';'IO' 'FC4';...
  753. 'IO' 'CP3';'IO' 'CP4';'IO' 'PO3';'IO' 'PO4';'IO' 'F5';'IO' 'F6';'IO' 'C5';...
  754. 'IO' 'C6';'IO' 'P5';'IO' 'P6';'IO' 'AF7';'IO' 'AF8';'IO' 'FT7';'IO' 'FT8';...
  755. 'IO' 'TP7';'IO' 'TP8';'IO' 'PO7';'IO' 'PO8';'IO' 'Fpz';'IO' 'CPz';'IO' 'POz';...
  756. 'IO' 'Oz';'IO' 'FCz'};
  757. for i=1:length(subj)
  758. coh_r_po4_temp{1, i}= ft_connectivityanalysis(cfg, eeg_fft_r_temp{1, i});
  759. coh_r_po4_temp_val(i,:,:)=coh_r_po4_temp{1, i}.cohspctrm;
  760. end
  762. coh_l_po4_shuf(n,:,:)=mean(coh_l_po4_temp_val,1);
  763. coh_l_po3_shuf(n,:,:)=mean(coh_l_po3_temp_val,1);
  764. coh_r_po3_shuf(n,:,:)=mean(coh_r_po3_temp_val,1);
  765. coh_r_po4_shuf(n,:,:)=mean(coh_r_po4_temp_val,1);
  766. n
  767. end
  768. delete(parob);
  769. save('coh_shuf_fuse.mat','coh_l_po4_shuf','coh_l_po3_shuf','coh_r_po3_shuf','coh_r_po4_shuf');
  771. % 取百分位数
  772. load('coh_shuf_fuse.mat');
  773. coh_l_po3_shuf_temp=squeeze(coh_l_po3_shuf(:,:,22));
  774. coh_l_po4_shuf_temp=squeeze(coh_l_po4_shuf(:,:,13));
  775. coh_r_po3_shuf_temp=squeeze(coh_r_po3_shuf(:,:,22));
  776. coh_r_po4_shuf_temp=squeeze(coh_r_po4_shuf(:,:,13));
  778. coh_l_po3_val_m=mean(coh_l_po3_val,2);
  779. coh_l_po4_val_m=mean(coh_l_po4_val,2);
  780. coh_r_po3_val_m=mean(coh_r_po3_val,2);
  781. coh_r_po4_val_m=mean(coh_r_po4_val,2);
  783. for i=1:64
  784. coh_l_po3_p(i) = invprctile(coh_l_po3_shuf_temp(:,i),coh_l_po3_val_m(i));
  785. coh_l_po4_p(i) = invprctile(coh_l_po4_shuf_temp(:,i),coh_l_po4_val_m(i));
  786. coh_r_po3_p(i) = invprctile(coh_r_po3_shuf_temp(:,i),coh_r_po3_val_m(i));
  787. coh_r_po4_p(i) = invprctile(coh_r_po4_shuf_temp(:,i),coh_r_po4_val_m(i));
  788. end
  790. coh_l_p=(coh_l_po3_p+coh_l_po4_p)/2;
  791. coh_r_p=(coh_r_po3_p+coh_r_po4_p)/2;
  793. % topo plot
  794. chann_label=eegdata{1, 1}.label;
  795. chann_label(20)=[];
  797. cfg = [];
  798. cfg.layout = 'easycapM1.mat';
  799. cfg.xlim = 'maxmin'; % time limitation
  800. cfg.zlim = [50 100];
  801. cfg.style = 'straight';
  802. cfg.colormap = brewermap([],'Reds');
  803. cfg.colorbar = 'yes';
  804. % cfg.marker = 'on';
  805. % cfg.comment = 'xlim';
  806. % cfg.commentpos = 'lefttop';
  808. coh_l_plot = {};
  809. coh_l_plot.avg = coh_l_p'; % data definition, channel * time
  810. coh_l_plot.time = 1; % time definition
  811. coh_l_plot.dimord = 'chan_time';
  812. coh_l_plot.label = chann_label;
  814. cfg.highlight = 'on';
  815. cfg.highlightchannel = find(coh_l_p'>95);
  816. cfg.highlightsymbol = '*';
  817. cfg.highlightcolor = [1 1 0];
  818. % cfg.highlightsize = 6;
  820. ft_topoplotER(cfg, coh_l_plot);
  821. title('coh_l');
  822. axis tight;
  824. cfg = [];
  825. cfg.layout = 'easycapM1.mat';
  826. cfg.xlim = 'maxmin'; % time limitation
  827. cfg.zlim = [50 100];
  828. cfg.style = 'straight';
  829. cfg.colormap = brewermap([],'Reds');
  830. cfg.colorbar = 'yes';
  831. % cfg.marker = 'on';
  832. % cfg.comment = 'xlim';
  833. % cfg.commentpos = 'lefttop';
  835. coh_r_plot = {};
  836. coh_r_plot.avg = coh_r_p'; % data definition, channel * time
  837. coh_r_plot.time = 1; % time definition
  838. coh_r_plot.dimord = 'chan_time';
  839. coh_r_plot.label = chann_label;
  841. cfg.highlight = 'on';
  842. cfg.highlightchannel = find(coh_r_p'>95);
  843. cfg.highlightsymbol = '+';
  844. cfg.highlightcolor = [1 1 0];
  846. ft_topoplotER(cfg, coh_r_plot);
  847. title('coh_r');
  848. axis tight;
  850. save('coh_fuse.mat','coh_l_p','coh_r_p');
  852. %% 5. behaviral correlates，两个SSVEP的相位差异与行为之间的相关。circular-linear相关
  853. phase_diff=circ_mean([phase_l_diff; phase_r_diff]);
  855. load('D:\OneDrive - hznu.edu.cn\Matlab_workspace\Attention and oscillation\kongqing\Data\beha\exp1\dual_acc.mat');
  856. load('D:\OneDrive - hznu.edu.cn\Matlab_workspace\Attention and oscillation\kongqing\Data\beha\exp1\dual_rt.mat');
  858. % acc
  859. for i=1:length(subj)
  860. acc_sub(i)=mean(dual_acc{1, subj(i)});
  861. end
  863. % RT
  864. for i=1:length(subj)
  865. for j=1:320
  866. if dual_acc{1, subj(i)}(1,j) == 0
  867. dual_rt{1, subj(i)}(1,j) = NaN;
  868. end
  869. end
  870. rt_sub(i)=mean(dual_rt{1, subj(i)},"omitnan");
  871. end
  873. % correlation
  874. [rho pval] = circ_corrcl(phase_diff', rt_sub');
  875. [rho pval] = circ_corrcl(abs(phase_diff'), rt_sub');
  877. % plot
  878. scatter(phase_diff,rt_sub);
  879. scatter(abs(phase_diff),rt_sub);
  881. save('beha_fuse.mat','phase_diff','rt_sub');

fuse.m at commit 7d710bf, no license · at the source

Overview

Authors: Qing Kong1,2, Tong Wang1,2, Jianrong Jia1,2
  1. Department of Psychology, Hangzhou Normal University,Hangzhou, Zhejiang 311121 China
  2. Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University,Hangzhou, Zhejiang 311121 China
Institutions: Hangzhou Normal University (China)
Journal: BMC biology, volume 24, issue 1, article 154
Dates: received 6 January 2026; accepted 8 May 2026; published online 15 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s12915-026-02630-7 · PMID 42141460 · PMCID PMC13349037 · OpenAlex W7161305590
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials
Keywords: Visual attention, Rhythmic sampling, Task modulation, Phase modulation
MeSH: Attention*, Evoked Potentials, Visual*, Visual Perception*, Adult, Female, Humans, Male, Photic Stimulation, Young Adult (* major topic)
Topic: Flow Experience in Various Fields (Developmental and Educational Psychology, Psychology), according to OpenAlex
Citations: not cited yet (Europe PMC); 47 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 3 matches between paragraphs and lines of code.

perevo/attentiondynamicsKQ

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 7d710bfb81aa1aa2d384c0850e1263d189c83270, 13 July 2024
Languages: MATLAB (2)
Size: 2 files, 2 scripts
Software Heritage: not archived
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: CircStat (2 files), FieldTrip (2 files), Parallel Computing Toolbox (2 files), Statistics and Machine Learning Toolbox (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
2 files
  • fuse.m, MATLAB, 881 lines, 2 matches
  • sep.m, MATLAB, 879 lines, 1 match
At the source: github.com/perevo/attentiondynamicsKQ

The paper's code and data availability statement is in the Data section.

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Read it in the paper: doi.org/10.1186/s12915-026-02630-7.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 4 keywords, 9 MeSH terms, 45 references.

Cite

This paper

Kong, Q., Wang, T., & Jia, J. (2026). Phasic modulation of attentional rhythmic sampling according to task demands. BMC biology, 24(1), 154. https://doi.org/10.1186/s12915-026-02630-7

BibTeX

@article{kong2026phasic,
author = {Kong, Qing and Wang, Tong and Jia, Jianrong},
title = {{Phasic modulation of attentional rhythmic sampling according to task demands}},
journal = {BMC biology},
year = {2026},
month = may,
volume = {24},
number = {1},
pages = {154},
publisher = {BMC},
issn = {1741-7007},
doi = {10.1186/s12915-026-02630-7},
url = {https://doi.org/10.1186/s12915-026-02630-7},
pmid = {42141460},
pmcid = {PMC13349037}
}

RIS

TY - JOUR
AU - Kong, Qing
AU - Wang, Tong
AU - Jia, Jianrong
TI - Phasic modulation of attentional rhythmic sampling according to task demands
T2 - BMC biology
J2 - BMC Biol
PY - 2026
DA - 2026/05/15
VL - 24
IS - 1
SP - 154
SN - 1741-7007
PB - BMC
DO - 10.1186/s12915-026-02630-7
UR - https://doi.org/10.1186/s12915-026-02630-7
LA - en
ER -

CSL-JSON

{
"id": "10.1186/s12915-026-02630-7",
"type": "article-journal",
"title": "Phasic modulation of attentional rhythmic sampling according to task demands",
"container-title": "BMC biology",
"author": [
{
"family": "Kong",
"given": "Qing"
},
{
"family": "Wang",
"given": "Tong"
},
{
"family": "Jia",
"given": "Jianrong"
}
],
"container-title-short": "BMC Biol",
"volume": "24",
"issue": "1",
"page": "154",
"DOI": "10.1186/s12915-026-02630-7",
"PMID": "42141460",
"PMCID": "PMC13349037",
"ISSN": "1741-7007",
"publisher": "BMC",
"URL": "https://doi.org/10.1186/s12915-026-02630-7",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
15
]
]
}
}

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