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Different pattern: a new EEG-based method for mental performance detection.

Code ↔ Paper

6 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 6 matches
  1. [1] § Experimental results › Explainable results ↔ main.m, lines 140–180 · score 0.69 · DLob symbols, TL, TR, PL, PR, CL
  2. [2] § Experimental results › Experimental setup ↔ main.m, lines 223–292 · score 0.63 · cortical connectome, DLob sentence, DLob symbols, entropy, histogram, transition
  3. [3] § Discussions › External dataset validation ↔ main.m, lines 1–49 · score 0.60 · driven XFE model, EEG signal, DiffPat, detection, mental
  4. [4] § Experimental results › Explainable results ↔ main.m, lines 223–292 · score 0.59 · cortical connectome, DLob sentence, DLob symbol, histogram
  5. [5] § The presented DiffPat-driven XFE model › Overall pipeline overview ↔ ten_cv_inca.m, lines 114–157 · score 0.51 · iterative feature selection, feature weighting, nested, training, INCA, fold
  6. [6] § The presented DiffPat-driven XFE model › Overall pipeline overview ↔ loso_cv_inca.m, lines 62–113 · score 0.50 · iterative feature selection, feature weighting, LOSO, training, INCA, fold

Paper

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

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The authors' code

MATLAB · 292 lines · 11 KB · MIT · 4 matches

  1. % =========================================================================
  2. % Main Script: DiffPat-driven XFE Model for Mental Performance Detection
  3. % =========================================================================
  4. % "Different Pattern: A new mental performance detection using EEG signal"
  5. %
  6. % Referans:
  7. % Ince et al., "Different Pattern: A new mental performance detection
  8. % using EEG signal", 2026.
  9. % =========================================================================
  11. clc; clear; close all;
  13. fprintf('==========================================================\n');
  14. fprintf(' DiffPat-driven XFE Model - Mental Performance Detection\n');
  15. fprintf('==========================================================\n\n');
  17. % =====================================================================
  18. % 1. VERI YUKLEME
  19. % =====================================================================
  21. data_file = 'EEG_mental_performance.mat'; % <-- Veri dosyanizin adi
  23. if ~exist(data_file, 'file')
  24. error(['Veri dosyasi bulunamadi: %s\n' ...
  25. 'Lutfen veri dosyanizin yolunu data_file degiskeninde belirtin.\n' ...
  26. 'Beklenen degiskenler: data (cell/3D), labels (Nx1), subjects (Nx1)'], ...
  27. data_file);
  28. end
  30. fprintf('Veri yukleniyor: %s\n', data_file);
  31. loaded = load(data_file);
  34. if isfield(loaded, 'data') && isfield(loaded, 'labels') && isfield(loaded, 'subjects')
  35. raw_data = loaded.data;
  36. labels = loaded.labels(:);
  37. subject_ids = loaded.subjects(:);
  38. elseif isfield(loaded, 'X') && isfield(loaded, 'y') && isfield(loaded, 'subject_ids')
  40. fprintf('Hazir ozellik matrisi bulundu, DiffPat cikarimi atlanacak.\n');
  41. X = loaded.X;
  42. y = loaded.y(:);
  43. subject_ids = loaded.subject_ids(:);
  44. raw_data = [];
  45. labels = y;
  46. else
  47. error(['Veri dosyasinda beklenen degiskenler bulunamadi.\n' ...
  48. 'Beklenen: (data, labels, subjects) veya (X, y, subject_ids)']);
  49. end
  51. % =====================================================================
  52. % 2. DIFFPAT OZELLIK CIKARIMI
  53. % =====================================================================
  55. if ~isempty(raw_data)
  56. fprintf('\n--- DiffPat Ozellik Cikarimi ---\n');
  58. if iscell(raw_data)
  59. num_segments = length(raw_data);
  61. [~, num_channels] = size(raw_data{1});
  62. if size(raw_data{1}, 1) < size(raw_data{1}, 2)
  63. num_channels = size(raw_data{1}, 1);
  64. end
  65. elseif ndims(raw_data) == 3
  66. [~, num_channels, num_segments] = size(raw_data);
  67. else
  68. error('Veri formati desteklenmiyor. Cell array veya 3D matris olmali.');
  69. end
  71. num_features = num_channels^2;
  72. X = zeros(num_segments, num_features);
  73. y = labels;
  75. fprintf('Segment sayisi: %d | Kanal sayisi: %d | Ozellik sayisi: %d\n', ...
  76. num_segments, num_channels, num_features);
  78. feature_time = tic;
  79. for seg = 1:num_segments
  80. if iscell(raw_data)
  81. eeg_segment = raw_data{seg};
  82. else
  83. eeg_segment = raw_data(:, :, seg);
  84. end
  87. X(seg, :) = diffpat(eeg_segment);
  89. if mod(seg, 500) == 0
  90. fprintf(' Islem: %d / %d segment tamamlandi...\n', seg, num_segments);
  91. end
  92. end
  93. feat_elapsed = toc(feature_time);
  94. fprintf('DiffPat ozellik cikarimi tamamlandi: %.2f saniye\n', feat_elapsed);
  95. fprintf('Ozellik matrisi boyutu: %d x %d\n', size(X,1), size(X,2));
  96. end
  98. % =====================================================================
  99. % 3. VERI OZETI
  100. % =====================================================================
  101. fprintf('\n--- Veri Seti Ozeti ---\n');
  102. unique_labels = unique(y);
  103. num_subjects = length(unique(subject_ids));
  104. fprintf('Toplam segment: %d\n', length(y));
  105. fprintf('Toplam birey: %d\n', num_subjects);
  106. for lbl = 1:length(unique_labels)
  107. fprintf(' Class %d: %d segment\n', unique_labels(lbl), sum(y == unique_labels(lbl)));
  108. end
  110. % =====================================================================
  111. % 4. SINIFLANDIRMA: LOSO CV (Birincil Degerlendirme)
  112. % =====================================================================
  113. fprintf('\n');
  114. fprintf('==========================================================\n');
  115. fprintf(' LOSO Cross-Validation (Birincil Metrik)\n');
  116. fprintf('==========================================================\n');
  118. [loso_acc, loso_cm, loso_subj_results, loso_feat_info] = ...
  119. loso_cv_inca(X, y, subject_ids);
  121. % =====================================================================
  122. % 5. SINIFLANDIRMA: 10-Fold Subject-Aware CV (Tamamlayici Metrik)
  123. % =====================================================================
  124. fprintf('\n');
  125. fprintf('==========================================================\n');
  126. fprintf(' 10-Fold Subject-Aware Cross-Validation\n');
  127. fprintf('==========================================================\n');
  129. [tenfold_acc, tenfold_cm, tenfold_results, tenfold_feat_info] = ...
  130. ten_cv_inca(X, y, subject_ids);
  132. % =====================================================================
  133. % 6. DLOB-TABANLI XAI SONUCLARI
  134. % =====================================================================
  135. fprintf('\n');
  136. fprintf('==========================================================\n');
  137. fprintf(' DLob-tabanli Aciklanabilir Sonuclar (XAI)\n');
  138. fprintf('==========================================================\n');
  140. % DLob Look-Up Table (LUT): 32 kanal -> 14 DLob sembol
  141. % Emotiv Flex 32-kanal montaji icin kanal-sembol eslestirmesi
  142. % Semboller: FL, FR, CL, CR, TL, TR, PL, PR, OL, OR, Cz, Oz, Pz, Fz
  143. channel_names = {'Fp1','Fp2','F3','F4','F7','F8','FC1','FC2', ...
  144. 'FC5','FC6','C3','C4','T7','T8','CP1','CP2', ...
  145. 'CP5','CP6','P3','P4','P7','P8','PO3','PO4', ...
  146. 'O1','O2','Fz','Cz','Pz','Oz','AF3','AF4'};
  148. % LUT: Her kanal icin DLob sembol indeksi (1-14)
  149. % 1:FL, 2:FR, 3:CL, 4:CR, 5:TL, 6:TR, 7:PL, 8:PR,
  150. % 9:OL, 10:OR, 11:Cz, 12:Oz, 13:Pz, 14:Fz
  151. dlob_symbols = {'FL','FR','CL','CR','TL','TR','PL','PR', ...
  152. 'OL','OR','Cz','Oz','Pz','Fz'};
  154. % Kanal -> DLob sembol eslestirmesi
  155. % Fp1->FL, Fp2->FR, F3->FL, F4->FR, F7->FL, F8->FR, FC1->CL, FC2->CR
  156. % FC5->CL, FC6->CR, C3->CL, C4->CR, T7->TL, T8->TR, CP1->PL, CP2->PR
  157. % CP5->PL, CP6->PR, P3->PL, P4->PR, P7->PL, P8->PR, PO3->OL, PO4->OR
  158. % O1->OL, O2->OR, Fz->Fz, Cz->Cz, Pz->Pz, Oz->Oz, AF3->FL, AF4->FR
  159. LUT = [1, 2, 1, 2, 1, 2, 3, 4, ... % Fp1,Fp2,F3,F4,F7,F8,FC1,FC2
  160. 3, 4, 3, 4, 5, 6, 7, 8, ... % FC5,FC6,C3,C4,T7,T8,CP1,CP2
  161. 7, 8, 7, 8, 7, 8, 9, 10, ... % CP5,CP6,P3,P4,P7,P8,PO3,PO4
  162. 9, 10, 14, 11, 13, 12, 1, 2]; % O1,O2,Fz,Cz,Pz,Oz,AF3,AF4
  164. num_channels = 32;
  166. % LOSO CV sonuclarindan secilen feature indekslerini topla
  167. % (tum fold'lardan ortak secilen feature'lari analiz et)
  168. all_selected_features = [];
  169. for i = 1:length(loso_feat_info)
  170. all_selected_features = [all_selected_features; ...
  171. loso_feat_info(i).feature_indices(:)];
  172. end
  173. % En sik secilen feature'lari bul
  174. [unique_feats, ~, ic] = unique(all_selected_features);
  175. feat_counts = accumarray(ic, 1);
  176. [~, sort_idx] = sort(feat_counts, 'descend');
  178. % En onemli feature'larin kanal bilgilerini cikar (Eq. 16-20)
  179. fprintf('\n--- Secilen Feature''larin Kanal Analizi ---\n');
  180. fprintf('Toplam benzersiz secilen feature: %d\n', length(unique_feats));
  182. % DLob cumlesi olustur
  183. DS = {};
  184. for k = 1:min(201, length(sort_idx)) % Makaleye gore 201 feature
  185. feat_idx = unique_feats(sort_idx(k));
  187. % Eq. 17: Satir kanali (1-indexed)
  188. chan1 = floor((feat_idx - 1) / num_channels) + 1;
  189. chan1 = mod(chan1 - 1, num_channels) + 1;
  191. % Eq. 18: Sutun kanali (1-indexed)
  192. chan2 = mod(feat_idx - 1, num_channels) + 1;
  194. % Eq. 19-20: DLob sembollerine donustur
  195. DS{end+1} = dlob_symbols{LUT(chan1)};
  196. DS{end+1} = dlob_symbols{LUT(chan2)};
  197. end
  199. % DLob cumlesi
  200. dlob_sentence = strjoin(DS, ' ');
  201. fprintf('\nDLob Cumlesi (ilk 50 sembol):\n');
  202. disp(strjoin(DS(1:min(50, length(DS))), ' '));
  204. % DLob histogram
  205. fprintf('\n--- DLob Sembol Histogrami ---\n');
  206. symbol_counts = zeros(1, length(dlob_symbols));
  207. for s = 1:length(DS)
  208. for sym = 1:length(dlob_symbols)
  209. if strcmp(DS{s}, dlob_symbols{sym})
  210. symbol_counts(sym) = symbol_counts(sym) + 1;
  211. break;
  212. end
  213. end
  214. end
  216. for sym = 1:length(dlob_symbols)
  217. if symbol_counts(sym) > 0
  218. fprintf(' %-4s: %d (%.2f%%)\n', dlob_symbols{sym}, ...
  219. symbol_counts(sym), symbol_counts(sym)/length(DS)*100);
  220. end
  221. end
  223. % Bilgi entropisi
  224. probs = symbol_counts / sum(symbol_counts);
  225. probs = probs(probs > 0); % Sifir olasiliklari cikar
  226. info_entropy = -sum(probs .* log2(probs));
  227. max_entropy = log2(length(dlob_symbols));
  228. complexity_ratio = info_entropy / max_entropy * 100;
  230. fprintf('\nBilgi Entropisi: %.4f bit\n', info_entropy);
  231. fprintf('Maksimum Entropi: %.4f bit (log2(%d))\n', max_entropy, length(dlob_symbols));
  232. fprintf('Karmasiklik Orani: %.2f%%\n', complexity_ratio);
  234. % Cortical connectome (transition table)
  235. fprintf('\n--- Cortical Connectome Transition Table ---\n');
  236. connectome = zeros(length(dlob_symbols));
  237. for s = 1:length(DS)-1
  238. from_idx = find(strcmp(dlob_symbols, DS{s}));
  239. to_idx = find(strcmp(dlob_symbols, DS{s+1}));
  240. if ~isempty(from_idx) && ~isempty(to_idx)
  241. connectome(from_idx, to_idx) = connectome(from_idx, to_idx) + 1;
  242. end
  243. end
  245. fprintf(' ');
  246. for sym = 1:length(dlob_symbols)
  247. fprintf('%-5s', dlob_symbols{sym});
  248. end
  249. fprintf('\n');
  250. for i = 1:length(dlob_symbols)
  251. fprintf('%-5s: ', dlob_symbols{i});
  252. for j = 1:length(dlob_symbols)
  253. fprintf('%-5d', connectome(i,j));
  254. end
  255. fprintf('\n');
  256. end
  258. % =====================================================================
  259. % 7. SONUC OZETI
  260. % =====================================================================
  261. fprintf('\n');
  262. fprintf('==========================================================\n');
  263. fprintf(' GENEL SONUC OZETI\n');
  264. fprintf('==========================================================\n');
  265. fprintf('LOSO CV Dogruluk (Birincil): %.2f%%\n', loso_acc);
  266. fprintf('10-Fold CV Dogruluk: %.2f%%\n', tenfold_acc);
  267. fprintf('DLob Bilgi Entropisi: %.4f bit\n', info_entropy);
  268. fprintf('DLob Karmasiklik Orani: %.2f%%\n', complexity_ratio);
  269. fprintf('==========================================================\n');
  271. % =====================================================================
  272. % 8. SONUCLARI KAYDET
  273. % =====================================================================
  274. results.loso_accuracy = loso_acc;
  275. results.loso_cm = loso_cm;
  276. results.loso_subject_results = loso_subj_results;
  277. results.loso_feature_info = loso_feat_info;
  278. results.tenfold_accuracy = tenfold_acc;
  279. results.tenfold_cm = tenfold_cm;
  280. results.tenfold_results = tenfold_results;
  281. results.tenfold_feature_info = tenfold_feat_info;
  282. results.dlob_sentence = dlob_sentence;
  283. results.dlob_histogram = symbol_counts;
  284. results.dlob_entropy = info_entropy;
  285. results.connectome = connectome;
  286. results.feature_matrix = X;
  287. results.labels = y;
  288. results.subject_ids = subject_ids;
  290. save('DiffPat_XFE_results.mat', 'results', '-v7.3');
  291. fprintf('\nSonuclar kaydedildi: DiffPat_XFE_results.mat\n');
  292. fprintf('==========================================================\n');

main.m at commit b00514d, under MIT · at the source

Overview

Authors: Ugur Ince1, Serkan Kirik2, Irem Tasci3, Prabal Datta Barua4, Mehmet Baygin5, Burak Tasci6, Sengul Dogan1, Turker Tuncer1
  1. Department of Digital Forensics Engineering, College of Technology, Firat University,Elazig, 23119 Türkiye
  2. Department of Pediatrics, Division of Pediatric Neurology, Fethi Sekin City Hospital, Elazig, 23280 Türkiye
  3. Department of Neurology, School of Medicine, Firat University,Elazig, Türkiye
  4. School of Business (Information System), University of Southern Queensland,Springfield, QLD 4350 Australia
  5. Department of Computer Engineering, Faculty of Engineering, Erzurum Technical University,Erzurum, Türkiye
  6. Vocational School of Technical Sciences, Firat University,Elazig, Türkiye
Institutions: Fırat University (Türkiye); University of Southern Queensland (Australia); Erzurum Technical University (Türkiye)
Journal: BMC medical informatics and decision making, volume 26, issue 1, article 290
Dates: received 17 March 2026; accepted 20 May 2026; published online 29 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s12911-026-03591-1 · PMID 42216207 · PMCID PMC13440034 · OpenAlex W7162785306
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), methods / tools (subfield)
Methods: Preprocessing
Keywords: Different Pattern, Directed Lobish, XFE, EEG signal classification, Neuroscience
MeSH: Electroencephalography*, Algorithms, Humans (* major topic)
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (123E357)
Citations: not cited yet (Europe PMC); 54 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 6 matches between paragraphs and lines of code.

mhmtbygn/Mental_Perfomance_Classification

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: b00514df76e1e0fef4297c7234b0c8d3579fff7b, 7 April 2026
Languages: MATLAB (4)
Size: 5 files, 4 scripts
Software Heritage: not archived
Found in: the text, “Experimental setup”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: 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
5 files
At the source: github.com/mhmtbygn/Mental_Perfomance_Classification

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  • 4 scripts, each with its path and the digest of its content;
  • 6 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

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Data availability statement

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Read it in the paper: doi.org/10.1186/s12911-026-03591-1.

Versions

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Version 2, 28 September 2026

  • Funding: added Türkiye Bilimsel ve Teknolojik Araştırma Kurumu: 123E357

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 5 keywords, 3 MeSH terms, 47 references.

Cite

This paper

Ince, U., Kirik, S., Tasci, I., Barua, P. D., Baygin, M., Tasci, B., Dogan, S., & Tuncer, T. (2026). Different pattern: a new EEG-based method for mental performance detection. BMC medical informatics and decision making, 26(1), 290. https://doi.org/10.1186/s12911-026-03591-1

BibTeX

@article{ince2026different,
author = {Ince, Ugur and Kirik, Serkan and Tasci, Irem and Barua, Prabal Datta and Baygin, Mehmet and Tasci, Burak and Dogan, Sengul and Tuncer, Turker},
title = {{Different pattern: a new EEG-based method for mental performance detection}},
journal = {BMC medical informatics and decision making},
year = {2026},
month = may,
volume = {26},
number = {1},
pages = {290},
publisher = {BMC},
issn = {1472-6947},
doi = {10.1186/s12911-026-03591-1},
url = {https://doi.org/10.1186/s12911-026-03591-1},
pmid = {42216207},
pmcid = {PMC13440034}
}

RIS

TY - JOUR
AU - Ince, Ugur
AU - Kirik, Serkan
AU - Tasci, Irem
AU - Barua, Prabal Datta
AU - Baygin, Mehmet
AU - Tasci, Burak
AU - Dogan, Sengul
AU - Tuncer, Turker
TI - Different pattern: a new EEG-based method for mental performance detection
T2 - BMC medical informatics and decision making
J2 - BMC Med Inform Decis Mak
PY - 2026
DA - 2026/05/29
VL - 26
IS - 1
SP - 290
SN - 1472-6947
PB - BMC
DO - 10.1186/s12911-026-03591-1
UR - https://doi.org/10.1186/s12911-026-03591-1
LA - en
ER -

CSL-JSON

{
"id": "10.1186/s12911-026-03591-1",
"type": "article-journal",
"title": "Different pattern: a new EEG-based method for mental performance detection",
"container-title": "BMC medical informatics and decision making",
"author": [
{
"family": "Ince",
"given": "Ugur"
},
{
"family": "Kirik",
"given": "Serkan"
},
{
"family": "Tasci",
"given": "Irem"
},
{
"family": "Barua",
"given": "Prabal Datta"
},
{
"family": "Baygin",
"given": "Mehmet"
},
{
"family": "Tasci",
"given": "Burak"
},
{
"family": "Dogan",
"given": "Sengul"
},
{
"family": "Tuncer",
"given": "Turker"
}
],
"container-title-short": "BMC Med Inform Decis Mak",
"volume": "26",
"issue": "1",
"page": "290",
"DOI": "10.1186/s12911-026-03591-1",
"PMID": "42216207",
"PMCID": "PMC13440034",
"ISSN": "1472-6947",
"publisher": "BMC",
"URL": "https://doi.org/10.1186/s12911-026-03591-1",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
29
]
]
}
}

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