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The experiment of the Mozart effect revisited: Modulations of the functional network according to EEG correlations.

Code ↔ Paper

2 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 2 matches
  1. [1] § Method details › Method validation ↔ layout2x3_desviaciones_DulceMariaGuerreroTanori.m, lines 1–57 · score 0.66 · cutting paper, open eyes, Symbol search, Stroop, Folding, 35 Hz
  2. [2] § Method details › Method validation ↔ layout2x3_desviaciones_DulceMariaGuerreroTanori.m, lines 1–57 · score 0.60 · cutting paper, open eye, Symbol search, Stroop, Folding, Music

Paper

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The authors' code

MATLAB · 446 lines · 15 KB · CC-BY-NC-4.0 · 2 matches

  1. %Matlab code by M.C. Dulce María Guerrero Tánori. If you use this code, please cite the associated article.
  3. %Code to calculate SCP deviations in 3 subjects under different conditions (Silence, Mozart, Brahms)
  4. % and in 5 different states (Eyes open, musical stimulus, symbol search task,
  5. % paper folding and cutting task, and Stroop effect task).
  6. % The result is printed to a .png file at 600 dpi in a 2x3 layout style.
  7. close all; clear; clc;
  9. fsample = 250;
  11. output_folder = 'C:\Users\...';
  12. if ~exist(output_folder,'dir')
  13. mkdir(output_folder);
  14. end
  15. labels5 = {'Open eyes','Music','Symbol search','Folding and cutting paper','Stroop effect'};
  17. %Channels in the desired order
  18. channels_selected = {'Fp1','F3','C3','P3','F7','T3','T5','O1', ...
  19. 'Fz','Cz','Pz','Fp2','F4','C4','P4','F8', ...
  20. 'T4','T6','O2'};
  21. channels = numel(channels_selected);
  23. %bands
  24. freq_high = [1,4,8,12.5,1];
  25. freq_low = [3.5,7.5,12,35,35];
  27. win_sec = 10; % windows of n seconds, in this case 10 seconds
  28. win_samp = round(win_sec*fsample);
  29. nwin = 20; %number of windows
  30. rng_seed = 123; %seed
  32. pairs_idx = nchoosek(1:5,2);
  33. npairs = size(pairs_idx,1);
  35. pair_names = cell(npairs,1);
  36. for k = 1:npairs
  37. i = pairs_idx(k,1);
  38. j = pairs_idx(k,2);
  39. pair_names{k} = sprintf('%s vs %s', labels5{i}, labels5{j});
  40. end
  42. pair_colors_fixed = [
  43. 0.00 0.45 0.74 % 1 blue
  44. 0.85 0.33 0.10 % 2 orange
  45. 0.93 0.69 0.13 % 3 yellow
  46. 0.49 0.18 0.56 % 4 purple
  47. 0.47 0.67 0.19 % 5 green
  48. 0.30 0.75 0.93 % 6 cyan
  49. 0.64 0.08 0.18 % 7 burgundy
  50. 0.25 0.25 0.25 % 8 dark grey
  51. 1.00 0.00 1.00 % 9 magenta
  52. 0.00 0.60 0.50 % 10 teal
  53. ];
  54. % SILENCE_data
  55. folders_A = {
  56. 'C:\users...\'
  57. };
  59. % MOZART_data
  60. folders_B = {
  61. 'C:\users...\'
  62. };
  64. % BRAHMS_data
  65. folders_C = {
  66. 'C:\users...\'
  67. };
  69. if numel(folders_A) ~= 5 || numel(folders_B) ~= 5 || numel(folders_C) ~= 5
  70. error('Cada conjunto de carpetas (A, B, C) debe tener exactamente 5 carpetas.');
  71. end
  73. mask = triu(true(channels),1); %upper triangle
  75. % correlation calculation block_silence
  76. fprintf('\n==================== SILENCIO ====================\n');
  77. Corr_5_A = zeros(channels, channels, 5);
  79. for iState = 1:5
  80. folder = folders_A{iState};
  81. files = dir(fullfile(folder,'*.set'));
  82. if isempty(files)
  83. error('No hay .set en %s', folder);
  84. end
  86. setfile = files(1).name;
  87. fprintf('Procesando SILENCIO | %s (%d/5): %s\n', labels5{iState}, iState, fullfile(folder,setfile));
  89. EEG = pop_loadset('filename', setfile, 'filepath', folder);
  91. file_labels = {EEG.chanlocs.labels};
  92. [tf, idx] = ismember(lower(channels_selected), lower(file_labels));
  93. if any(~tf)
  94. missing = channels_selected(~tf);
  95. error('Faltan canales en %s: %s', setfile, strjoin(missing,', '));
  96. end
  97. EEG2 = double(EEG.data(idx,:));
  99. FreqBands = 5; %broand band selected
  101. [B_high,A_high] = butter(4, freq_high(FreqBands)/(0.5*fsample),'high');
  102. EEG_high = filtfilt(B_high,A_high,EEG2'); %removes high frequencies
  104. [B_low,A_low] = butter(4, freq_low(FreqBands)/(0.5*fsample),'low');
  105. EEG_filter = filtfilt(B_low,A_low,EEG_high); %removes low frequencies
  107. Ref_median = median(EEG_filter,2);
  108. EEG_median = EEG_filter - Ref_median; %re-reference to the median
  110. Tlen = size(EEG_median,1);
  111. if Tlen < win_samp
  112. error('Archivo %s: no alcanza para una ventana de %d muestras.', setfile, win_samp);
  113. end
  115. maxStart = Tlen - win_samp + 1;
  116. rng(rng_seed + iState); %compute how many distinct points can be the start of a complete window
  118. if maxStart < nwin
  119. error('No hay suficientes posiciones (%d) para %d ventanas en %s', maxStart, nwin, setfile);
  120. end
  122. starts = randperm(maxStart, nwin).'; %generates random positions and chooses one result to be the start of a window
  124. % the EEG windows are extracted and concatenated into a single matrix
  125. % to subsequently calculate the correlation matrix between channels
  126. EEG_concat = zeros(nwin*win_samp, channels);
  127. for w = 1:nwin
  128. seg = EEG_median(starts(w):starts(w)+win_samp-1, :);
  129. EEG_concat((w-1)*win_samp + (1:win_samp), :) = seg;
  130. end
  132. CorrMatrix = corrcoef(EEG_concat);
  133. CorrMatrix(1:channels+1:end) = 1;
  135. Corr_5_A(:,:,iState) = CorrMatrix;
  136. end
  138. % correlation calculation block_Mozart
  139. fprintf('\n==================== MOZART ====================\n');
  140. Corr_5_B = zeros(channels, channels, 5);
  142. for iState = 1:5
  143. folder = folders_B{iState};
  144. files = dir(fullfile(folder,'*.set'));
  145. if isempty(files)
  146. error('No hay .set en %s', folder);
  147. end
  149. setfile = files(1).name;
  150. fprintf('Procesando MOZART | %s (%d/5): %s\n', labels5{iState}, iState, fullfile(folder,setfile));
  152. EEG = pop_loadset('filename', setfile, 'filepath', folder);
  154. file_labels = {EEG.chanlocs.labels};
  155. [tf, idx] = ismember(lower(channels_selected), lower(file_labels));
  156. if any(~tf)
  157. missing = channels_selected(~tf);
  158. error('Faltan canales en %s: %s', setfile, strjoin(missing,', '));
  159. end
  160. EEG2 = double(EEG.data(idx,:));
  162. FreqBands = 5;
  164. [B_high,A_high] = butter(4, freq_high(FreqBands)/(0.5*fsample),'high');
  165. EEG_high = filtfilt(B_high,A_high,EEG2');
  167. [B_low,A_low] = butter(4, freq_low(FreqBands)/(0.5*fsample),'low');
  168. EEG_filter = filtfilt(B_low,A_low,EEG_high);
  170. Ref_median = median(EEG_filter,2);
  171. EEG_median = EEG_filter - Ref_median;
  173. Tlen = size(EEG_median,1);
  174. if Tlen < win_samp
  175. error('Archivo %s: no alcanza para una ventana de %d muestras.', setfile, win_samp);
  176. end
  178. maxStart = Tlen - win_samp + 1;
  179. rng(rng_seed + iState);
  181. if maxStart < nwin
  182. error('No hay suficientes posiciones (%d) para %d ventanas en %s', maxStart, nwin, setfile);
  183. end
  185. starts = randperm(maxStart, nwin).';
  187. EEG_concat = zeros(nwin*win_samp, channels);
  188. for w = 1:nwin
  189. seg = EEG_median(starts(w):starts(w)+win_samp-1, :);
  190. EEG_concat((w-1)*win_samp + (1:win_samp), :) = seg;
  191. end
  193. CorrMatrix = corrcoef(EEG_concat);
  194. CorrMatrix(1:channels+1:end) = 1;
  196. Corr_5_B(:,:,iState) = CorrMatrix;
  197. end
  199. % correlation calculation block_Brahms
  200. fprintf('\n==================== BRAHMS ====================\n');
  201. Corr_5_C = zeros(channels, channels, 5);
  203. for iState = 1:5
  204. folder = folders_C{iState};
  205. files = dir(fullfile(folder,'*.set'));
  206. if isempty(files)
  207. error('No hay .set en %s', folder);
  208. end
  210. setfile = files(1).name;
  211. fprintf('Procesando BRAHMS | %s (%d/5): %s\n', labels5{iState}, iState, fullfile(folder,setfile));
  213. EEG = pop_loadset('filename', setfile, 'filepath', folder);
  215. file_labels = {EEG.chanlocs.labels};
  216. [tf, idx] = ismember(lower(channels_selected), lower(file_labels));
  217. if any(~tf)
  218. missing = channels_selected(~tf);
  219. error('Faltan canales en %s: %s', setfile, strjoin(missing,', '));
  220. end
  221. EEG2 = double(EEG.data(idx,:));
  223. FreqBands = 5;
  225. [B_high,A_high] = butter(4, freq_high(FreqBands)/(0.5*fsample),'high');
  226. EEG_high = filtfilt(B_high,A_high,EEG2');
  228. [B_low,A_low] = butter(4, freq_low(FreqBands)/(0.5*fsample),'low');
  229. EEG_filter = filtfilt(B_low,A_low,EEG_high);
  231. Ref_median = median(EEG_filter,2);
  232. EEG_median = EEG_filter - Ref_median;
  234. Tlen = size(EEG_median,1);
  235. if Tlen < win_samp
  236. error('Archivo %s: no alcanza para una ventana de %d muestras.', setfile, win_samp);
  237. end
  239. maxStart = Tlen - win_samp + 1;
  240. rng(rng_seed + iState);
  242. if maxStart < nwin
  243. error('No hay suficientes posiciones (%d) para %d ventanas en %s', maxStart, nwin, setfile);
  244. end
  246. starts = randperm(maxStart, nwin).';
  248. EEG_concat = zeros(nwin*win_samp, channels);
  249. for w = 1:nwin
  250. seg = EEG_median(starts(w):starts(w)+win_samp-1, :);
  251. EEG_concat((w-1)*win_samp + (1:win_samp), :) = seg;
  252. end
  254. CorrMatrix = corrcoef(EEG_concat);
  255. CorrMatrix(1:channels+1:end) = 1;
  257. Corr_5_C(:,:,iState) = CorrMatrix;
  258. end
  260. % First, an average connectivity matrix is ​​calculated across the five states: SCP.
  261. %Then, this average is subtracted from each state to determine its deviation.
  262. % Next, these deviations are compared pairwise using correlation.
  263. % Finally, these values ​​are sorted to construct a cumulative
  264. % distribution.(SILENCE)
  265. SCP_A = mean(Corr_5_A,3);
  266. D_5_A = zeros(channels, channels, 5);
  268. for iState = 1:5
  269. D_5_A(:,:,iState) = Corr_5_A(:,:,iState) - SCP_A;
  270. end
  272. R_pairs_A = zeros(npairs,1);
  273. for k = 1:npairs
  274. i = pairs_idx(k,1);
  275. j = pairs_idx(k,2);
  276. v1 = D_5_A(:,:,i);
  277. v2 = D_5_A(:,:,j);
  278. R_pairs_A(k) = corr(v1(mask), v2(mask));
  279. end
  281. [x_sorted_A, idx_sorted_A] = sort(R_pairs_A(:));
  282. y_A = (1:numel(x_sorted_A)).'/numel(x_sorted_A);
  284. % First, an average connectivity matrix is ​​calculated across the five states: SCP.
  285. %Then, this average is subtracted from each state to determine its deviation.
  286. % Next, these deviations are compared pairwise using correlation.
  287. % Finally, these values ​​are sorted to construct a cumulative
  288. % distribution.(MOZART)
  289. SCP_B = mean(Corr_5_B,3);
  290. D_5_B = zeros(channels, channels, 5);
  292. for iState = 1:5
  293. D_5_B(:,:,iState) = Corr_5_B(:,:,iState) - SCP_B;
  294. end
  296. R_pairs_B = zeros(npairs,1);
  297. for k = 1:npairs
  298. i = pairs_idx(k,1);
  299. j = pairs_idx(k,2);
  300. v1 = D_5_B(:,:,i);
  301. v2 = D_5_B(:,:,j);
  302. R_pairs_B(k) = corr(v1(mask), v2(mask));
  303. end
  305. [x_sorted_B, idx_sorted_B] = sort(R_pairs_B(:));
  306. y_B = (1:numel(x_sorted_B)).'/numel(x_sorted_B);
  308. % First, an average connectivity matrix is ​​calculated across the five states: SCP.
  309. %Then, this average is subtracted from each state to determine its deviation.
  310. % Next, these deviations are compared pairwise using correlation.
  311. % Finally, these values ​​are sorted to construct a cumulative
  312. % distribution.(BRAHMS)
  313. SCP_C = mean(Corr_5_C,3);
  314. D_5_C = zeros(channels, channels, 5);
  316. for iState = 1:5
  317. D_5_C(:,:,iState) = Corr_5_C(:,:,iState) - SCP_C;
  318. end
  320. R_pairs_C = zeros(npairs,1);
  321. for k = 1:npairs
  322. i = pairs_idx(k,1);
  323. j = pairs_idx(k,2);
  324. v1 = D_5_C(:,:,i);
  325. v2 = D_5_C(:,:,j);
  326. R_pairs_C(k) = corr(v1(mask), v2(mask));
  327. end
  329. [x_sorted_C, idx_sorted_C] = sort(R_pairs_C(:));
  330. y_C = (1:numel(x_sorted_C)).'/numel(x_sorted_C);
  332. % Saving tables
  333. T_A = table(pair_names, R_pairs_A, 'VariableNames', {'Pair','CorrDeviationMatrices'});
  334. T_B = table(pair_names, R_pairs_B, 'VariableNames', {'Pair','CorrDeviationMatrices'});
  335. T_C = table(pair_names, R_pairs_C, 'VariableNames', {'Pair','CorrDeviationMatrices'});
  337. writetable(T_A, fullfile(output_folder,'R_pairs_DesvSCP_SILENCIO.csv'));
  338. writetable(T_B, fullfile(output_folder,'R_pairs_DesvSCP_MOZART.csv'));
  339. writetable(T_C, fullfile(output_folder,'R_pairs_DesvSCP_BRAHMS.csv'));
  341. save(fullfile(output_folder,'R_pairs_DesvSCP_layout_3condiciones.mat'), ...
  342. 'Corr_5_A','Corr_5_B','Corr_5_C', ...
  343. 'SCP_A','SCP_B','SCP_C', ...
  344. 'D_5_A','D_5_B','D_5_C', ...
  345. 'R_pairs_A','R_pairs_B','R_pairs_C', ...
  346. 'x_sorted_A','x_sorted_B','x_sorted_C', ...
  347. 'idx_sorted_A','idx_sorted_B','idx_sorted_C', ...
  348. 'pair_names','pairs_idx','pair_colors_fixed','labels5');
  350. % Layout 2X3 figure
  351. figure('Color','w','Position',[100 100 1500 550])
  353. t = tiledlayout(1,3);
  354. t.TileSpacing = 'compact';
  355. t.Padding = 'compact';
  357. % Plot the empirical cumulative distribution function (CDF) of the correlations
  358. % between deviation matrices for the Silence condition. Each point corresponds
  359. % to a pair of states, and colors indicate the specific state pair.
  360. % The gray step line represents the cumulative distribution of all pairwise
  361. % values.(SILENCE)
  362. nexttile
  363. stairs(x_sorted_A, y_A, 'LineWidth',2.5,'Color',[0.5 0.5 0.5]); hold on
  365. for kk = 1:numel(x_sorted_A)
  366. this_pair_idx = idx_sorted_A(kk);
  367. this_color = pair_colors_fixed(this_pair_idx,:);
  368. plot(x_sorted_A(kk), y_A(kk), 'o', ...
  369. 'MarkerFaceColor', this_color, ...
  370. 'MarkerEdgeColor', 'k', ...
  371. 'MarkerSize', 12);
  372. end
  374. grid on
  375. xlim([-1 1])
  376. ylim([0 1])
  377. text(-0.08,1.02,'D','Units','normalized','FontSize',15,'FontWeight','bold')
  378. xlabel('Correlation between deviation matrices')
  379. ylabel('Cumulative proportion')
  381. % Plot the empirical cumulative distribution function (CDF) of the correlations
  382. % between deviation matrices for the Silence condition. Each point corresponds
  383. % to a pair of states, and colors indicate the specific state pair.
  384. % The gray step line represents the cumulative distribution of all pairwise
  385. % values. (MOZART)
  386. nexttile
  387. stairs(x_sorted_B, y_B, 'LineWidth',2.5,'Color',[0.5 0.5 0.5]); hold on
  389. for kk = 1:numel(x_sorted_B)
  390. this_pair_idx = idx_sorted_B(kk);
  391. this_color = pair_colors_fixed(this_pair_idx,:);
  392. plot(x_sorted_B(kk), y_B(kk), 'o', ...
  393. 'MarkerFaceColor', this_color, ...
  394. 'MarkerEdgeColor', 'k', ...
  395. 'MarkerSize', 12);
  396. end
  398. grid on
  399. xlim([-1 1])
  400. ylim([0 1])
  401. text(-0.08,1.02,'E','Units','normalized','FontSize',15,'FontWeight','bold')
  402. xlabel('Correlation between deviation matrices')
  403. ylabel('Cumulative proportion')
  405. % Plot the empirical cumulative distribution function (CDF) of the correlations
  406. % between deviation matrices for the Silence condition. Each point corresponds
  407. % to a pair of states, and colors indicate the specific state pair.
  408. % The gray step line represents the cumulative distribution of all pairwise
  409. % values. (BRAHMS)
  410. nexttile
  411. stairs(x_sorted_C, y_C, 'LineWidth',2.5,'Color',[0.5 0.5 0.5]); hold on
  413. for kk = 1:numel(x_sorted_C)
  414. this_pair_idx = idx_sorted_C(kk);
  415. this_color = pair_colors_fixed(this_pair_idx,:);
  416. plot(x_sorted_C(kk), y_C(kk), 'o', ...
  417. 'MarkerFaceColor', this_color, ...
  418. 'MarkerEdgeColor', 'k', ...
  419. 'MarkerSize', 12);
  420. end
  422. grid on
  423. xlim([-1 1])
  424. ylim([0 1])
  425. text(-0.08,1.02,'F','Units','normalized','FontSize',15,'FontWeight','bold')
  426. xlabel('Correlation between deviation matrices')
  427. ylabel('Cumulative proportion')
  429. % Legends
  430. h = gobjects(npairs,1);
  431. for k = 1:npairs
  432. h(k) = plot(nan, nan, 'o', ...
  433. 'MarkerFaceColor', pair_colors_fixed(k,:), ...
  434. 'MarkerEdgeColor', 'k', ...
  435. 'MarkerSize', 12);
  436. end
  438. lgd = legend(h, pair_names);
  439. lgd.Layout.Tile = 'south';
  440. lgd.NumColumns = 2;
  441. lgd.FontSize = 9;
  443. % Saving png 600 dpi
  444. exportgraphics(gcf, ...
  445. fullfile(output_folder,'CDF_DesvSCP_layout_3condiciones_desvalSCP.png'), ...
  446. 'Resolution', 600);

layout2x3_desviaciones_DulceMariaGuerreroTanori.m at commit 61c4176, under CC-BY-NC-4.0 · at the source

Overview

Authors: Dulce María Guerrero-Tánori1, Antonieta Martínez-Guerrero2, Markus F. Müller2,3,4,5, Paola V. Olguín-Rodríguez3,5
ORCID iDs: Paola V. Olguín-Rodríguez
  1. Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos (UAEM), Cuernavaca, Mexico
  2. Facultad de Medicina, Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico
  3. Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos (UAEM), Cuernavaca, Mexico
  4. Centro Internacional de Ciencias A.C., Cuernavaca, Mexico
  5. Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico
Institutions: Universidad Autónoma del Estado de Morelos (Mexico); Universidad Nacional Autónoma de México (Mexico)
Journal: MethodsX, volume 16, article 103964
Dates: received 13 April 2026; accepted 18 May 2026; published online 19 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.mex.2026.103964 · PMID 42233052 · PMCID PMC13224368 · OpenAlex W7161737902
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality)
Methods: Spectral & time-frequency, Preprocessing, Smoothing, state filtering, decompositions, Machine learning, Connectivity, fMRI & imaging
Keywords: Mozart effect, Electroencephalography, Functional network, Stationary correlation pattern, Deviation matrices
Journal subjects: Psychology
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: author Dulce María Guerrero Tánori's doctoral scholarship (963085); SECIHTI
Citations: not cited yet (Europe PMC); 21 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

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Kanb7y/MethodsX_DulceMariaGuerreroTanori

License: CC-BY-NC-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 61c41765552d6773424545aa8e2925875c03fb34, 22 May 2026
Languages: MATLAB (2)
Size: 4 files, 2 scripts
Software Heritage: not archived
Found in: “Supplementary material and/or additional informa”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: EEGLAB (2 files), Signal Processing Toolbox (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
4 files
At the source: github.com/Kanb7y/MethodsX_DulceMariaGuerreroTanori

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Read it in the paper: doi.org/10.1016/j.mex.2026.103964.

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Version 2, 28 September 2026

  • Authors: added Paola V. Olguín-Rodríguez (0000-0002-4676-1089); removed Paola V. Olguín-Rodríguez

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 5 keywords, 2 funders, 18 references.

Cite

This paper

Guerrero-Tánori, D. M., Martínez-Guerrero, A., Müller, M. F., & Olguín-Rodríguez, P. V. (2026). The experiment of the Mozart effect revisited: Modulations of the functional network according to EEG correlations. MethodsX, 16, 103964. https://doi.org/10.1016/j.mex.2026.103964

BibTeX

@article{guerrerotanori2026experiment,
author = {Guerrero-Tánori, Dulce María and Martínez-Guerrero, Antonieta and Müller, Markus F. and Olguín-Rodríguez, Paola V.},
title = {{The experiment of the Mozart effect revisited: Modulations of the functional network according to EEG correlations}},
journal = {MethodsX},
year = {2026},
month = may,
volume = {16},
pages = {103964},
publisher = {Elsevier},
issn = {2215-0161},
doi = {10.1016/j.mex.2026.103964},
url = {https://doi.org/10.1016/j.mex.2026.103964},
pmid = {42233052},
pmcid = {PMC13224368}
}

RIS

TY - JOUR
AU - Guerrero-Tánori, Dulce María
AU - Martínez-Guerrero, Antonieta
AU - Müller, Markus F.
AU - Olguín-Rodríguez, Paola V.
TI - The experiment of the Mozart effect revisited: Modulations of the functional network according to EEG correlations
T2 - MethodsX
J2 - MethodsX
PY - 2026
DA - 2026/05/19
VL - 16
SP - 103964
SN - 2215-0161
PB - Elsevier
DO - 10.1016/j.mex.2026.103964
UR - https://doi.org/10.1016/j.mex.2026.103964
LA - en
ER -

CSL-JSON

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"author": [
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"family": "Guerrero-Tánori",
"given": "Dulce María"
},
{
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"given": "Antonieta"
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"volume": "16",
"page": "103964",
"DOI": "10.1016/j.mex.2026.103964",
"PMID": "42233052",
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"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.mex.2026.103964",
"language": "en",
"issued": {
"date-parts": [
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2026,
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}
}

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