OSCR

Intention-Action Conflict EEG-Hand Kinematics Dataset for Unimanual Control under Congruent and Incongruent Conditions.

Code ↔ Paper

15 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 15 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Data Pre-processing ↔ code/scripts/matlab/preprocess_eeg_pipeline.m, lines 1–133 · score 0.92 · amplitude threshold, zero phase FIR, ICLabel, pipeline, HEOG, M1
  2. [2] § Methods › Data Pre-processing ↔ code/scripts/matlab/preprocess_premove_55_14.m, lines 1–107 · score 0.88 · 0.1–45 Hz, ICLabel, HEOG, M1, M2, VEOG
  3. [3] § Methods › Data Pre-processing ↔ code/scripts/matlab/preprocess_eeg_pipeline.m, lines 1–133 · score 0.83 · preprocess eeg pipeline, amplitude threshold, baseline correction, ICA, event, segmented
  4. [4] § Methods › Data Pre-processing ↔ code/scripts/matlab/process_leap_dual_csv.m, lines 117–160 · score 0.71 · nearest neighbor, process leap dual, uniform, hit, trajectory, ball
  5. [5] § Methods › Data Pre-processing ↔ code/scripts/matlab/export_eeg_leap_aligned.m, lines 1–101 · score 0.68 · export eeg leap, cov, residual, global, segmented
  6. [6] § Methods › Data Pre-processing ↔ code/scripts/matlab/align_eeg_leap.m, lines 46–90 · score 0.67 · dynamic programming, align eeg leap, Leap trials, matched
  7. [7] § Methods › Data Pre-processing ↔ code/scripts/matlab/calibrate_pairs_with_events.m, the whole file · a weak match · score 0.67 · calibrate pairs, eeg leap, fitted, ratio, global, events
  8. [8] § Technical Validation › Neural and behavioral signal validation ↔ code/scripts/python/ersp_itc_trajectory.ipynb, lines 4–136 · score 0.63 · 2–30 Hz, baseline corrected, Morlet, ERSP, ITC, power
  9. [9] § Technical Validation › Readiness potentials (RPs) ↔ code/scripts/python/rp4pre_move.ipynb, lines 33–105 · score 0.62 · low pass filtered, rp4pre move, baseline, Python, EEG
  10. [10] § Data Records › Derived data ↔ code/scripts/matlab/preprocess_premove_55_14.m, lines 1–107 · score 0.60 · keep_mask_1s, extracted pre, variable, preprocessing, segment
  11. [11] § Data Records › Raw data › Leap motion ↔ code/scripts/matlab/process_leap_dual_csv.m, lines 1–39 · score 0.59 · ball_x_pix, move_direct, ball_color, mm, hit, leap
  12. [12] § Data Records › Raw data › Leap motion ↔ code/scripts/matlab/export_eeg_leap_aligned.m, lines 1–101 · score 0.59 · ball_x_pix, move_direct, ball_color, mm, hit, leap
  13. [13] § Data Records › Derived data ↔ code/scripts/matlab/preprocess_premove_55_14.m, lines 159–250 · score 0.55 · segs_var, Variable length, pre, EEG
  14. [14] § Technical Validation › Neural and behavioral signal validation ↔ code/scripts/python/ersp_itc_trajectory.ipynb, lines 138–165 · score 0.55 · ersp itc trajectory, hand trajectories, CI, axis, Python
  15. [15] § Methods › Data Pre-processing ↔ code/scripts/matlab/calibrate_pairs_with_events.m, the whole file · a weak match · score 0.53 · duration ratio, iterative, outlier, median

Paper

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The authors' code

MATLAB · 404 lines · 13 KB · CC-BY-4.0 · 3 matches

  1. function preprocess_premove_55_14(CDT_FILE, CSV_BALL, OUT_MAT, opts)
  2. % Preprocessing + Extracting pre-motion (55→14) segments + Reading CSV labels (color_ball / move_direct)
  3. % Save it as a .mat file in a specified path
  4. %
  5. % Required parameter:
  6. % CDT_FILE : .cdt file path
  7. % CSV_BALL : left_ball_1.csv path (including color_ball / move_direct /
  8. % trial / global_time)
  9. % OUT_MAT : The path of the output .mat file
  10. %
  11. % Optional parameters (opts) (with defaults):
  12. % .resample_hz = 200
  13. % .bp_lo_hz = 0.1
  14. % .bp_hi_hz = 50
  15. % .del_chans = {'HEO','VEOG','TRIGGER'}
  16. % .iclabel_thr = 0.90 % Eye/Muscle ≥ this probability is just removed
  17. % .reref_mode = 'average'
  18. % .pre_ms = 1000 % Fixed segment length (1s with 14 as the end)
  19. % .tol_ms_lo = 800 % Accept the lower time limit of 55→14
  20. % .tol_ms_hi = 1200 % Accept the upper time limit of 55→14
  22. if nargin < 4, opts = struct; end
  23. opts = setdef(opts, 'resample_hz', 200);
  24. opts = setdef(opts, 'bp_lo_hz', 0.1);
  25. opts = setdef(opts, 'bp_hi_hz', 45);
  26. opts = setdef(opts, 'del_chans', {'M1','M2','HEOG','VEOG','TRIGGER'});
  27. opts = setdef(opts, 'iclabel_thr', 0.90);
  28. opts = setdef(opts, 'reref_mode', 'average');
  29. opts = setdef(opts, 'pre_ms', 1000);
  30. opts = setdef(opts, 'tol_ms_lo', 800);
  31. opts = setdef(opts, 'tol_ms_hi', 1200);
  33. % Ensure that the output directory exists
  34. outdir = fileparts(OUT_MAT);
  35. if ~isempty(outdir) && ~exist(outdir,'dir'), mkdir(outdir); end
  37. % ---------------- 0) Load EEG ----------------
  38. fprintf('[0] Loading: %s\n', CDT_FILE);
  39. [~,~,ext] = fileparts(CDT_FILE);
  40. switch lower(ext)
  41. case '.set', EEG = pop_loadset(CDT_FILE);
  42. case {'.cdt','.dap','.dat','.rs3'}
  43. EEG = pop_loadcurry(CDT_FILE, 'CurryLocations','on', 'CurryEvents','on');
  44. otherwise, error('Unsupported EEG file: %s', ext);
  45. end
  46. EEG = eeg_checkset(EEG);
  48. % ---------------- 1) Electrode position -----------------
  49. fprintf('[1] Set channel locations...\n');
  50. EEG = try_set_chanlocs(EEG, '');
  52. % ---------------- 2) Downsample to 200 Hz -------------
  53. fprintf('[2] Resample to %d Hz...\n', opts.resample_hz);
  54. EEG = pop_resample(EEG, opts.resample_hz);
  56. % ---------------- 3) 0.1–45 Hz bandpass ------------
  57. fprintf('[3] Bandpass %.1f–%.1f Hz...\n', opts.bp_lo_hz, opts.bp_hi_hz);
  58. EEG = pop_eegfiltnew(EEG, opts.bp_lo_hz, opts.bp_hi_hz);
  60. % ---------------- 4) Delete channel --------------------
  61. fprintf('[4] Drop chans: %s\n', strjoin(opts.del_chans, ', '));
  62. EEG = delete_if_present(EEG, opts.del_chans);
  64. % ---------------- 5) ICA+ICLabel（Eye/Muscle）--
  65. fprintf('[5] ICA + ICLabel (Eye/Muscle >= %.2f)\n', opts.iclabel_thr);
  66. EEG = run_ica_remove_eye_muscle(EEG, opts.iclabel_thr);
  68. % ---------------- 6) Re-reference --------------------
  69. fprintf('[6] Re-reference: %s\n', opts.reref_mode);
  70. EEG = do_reref(EEG, opts.reref_mode);
  72. % ---------------- 7) Extract 55→14 segment ------------
  73. fprintf('[7] Extract 55→14 (var) & [-%d..0]ms (1s) segments...\n', opts.pre_ms);
  74. [segs_var, times_var, segs_1s, times_1s, trial_info, keep_mask_1s] = ...
  75. extract_premotor_segments(EEG, 55, 14, opts.pre_ms, [opts.tol_ms_lo opts.tol_ms_hi]);
  76. % ——Unify the 1s segment length to 200 points (C×200)
  77. [segs_1s, times_1s] = force_len_fixed(segs_1s, times_1s, 200, EEG.nbchan, EEG.srate);
  79. % ---------------- 8) Read CSV labels ----------------
  80. fprintf('[8] Read labels from CSV: %s\n', CSV_BALL);
  81. labels = read_ball_labels(CSV_BALL);
  83. % Align labels with EEG segments based on their "appearance order"
  84. n_eeg = numel(segs_var);
  85. n_lab = height(labels);
  86. m = min(n_eeg, n_lab);
  87. labels = labels(1:m, :);
  88. if n_eeg > m
  89. pad = table( (m+1:n_eeg)', repmat("",n_eeg-m,1), repmat("",n_eeg-m,1), ...
  90. 'VariableNames', {'trial_csv','color_ball','move_direct'});
  91. labels = [labels; pad];
  92. end
  94. % ---------------- Save to the specified .mat ----------------
  95. meta = struct();
  96. meta.srate = EEG.srate;
  97. meta.chanlabels = {EEG.chanlocs.labels}';
  98. meta.nbchan = EEG.nbchan;
  99. meta.preprocess = opts;
  101. save(OUT_MAT, 'segs_var','times_var','segs_1s','times_1s', ...
  102. 'trial_info','keep_mask_1s','labels','meta','-v7.3');
  103. fprintf('Saved: %s\n', OUT_MAT);
  104. fprintf('Done.\n');
  105. end
  107. % ====== helper function ======
  108. function s = setdef(s, f, v), if ~isfield(s, f), s.(f) = v; end, end
  110. function EEG = try_set_chanlocs(EEG, CHANLOC_FILE)
  111. try
  112. if ~isempty(CHANLOC_FILE) && exist(CHANLOC_FILE,'file')
  113. EEG = pop_chanedit(EEG, 'lookup', CHANLOC_FILE); return;
  114. end
  115. eeglabroot = fileparts(which('eeglab.m'));
  116. cands = { ...
  117. fullfile(eeglabroot,'plugins','dipfit','standard_BEM','elec','standard_1005.elc'), ...
  118. fullfile(eeglabroot,'plugins','dipfit','standard_BEM','elec','standard_1020.elc'), ...
  119. fullfile(eeglabroot,'sample_locs','standard-10-5-cap385.elp')};
  120. for i=1:numel(cands)
  121. if exist(cands{i},'file'), EEG = pop_chanedit(EEG, 'lookup', cands{i}); return; end
  122. end
  123. catch ME
  124. warning('Set chanlocs failed: %s', ME.message);
  125. end
  126. end
  128. function EEG = delete_if_present(EEG, chans)
  129. labels = {EEG.chanlocs.labels};
  130. mask = ismember(labels, chans);
  131. if any(mask)
  132. EEG = pop_select(EEG, 'nochannel', labels(mask));
  133. end
  134. end
  136. function EEG = run_ica_remove_eye_muscle(EEG, thr)
  137. EEG = pop_runica(EEG, 'icatype','runica','extended',1,'interrupt','off');
  138. if exist('pop_iclabel','file')==2
  139. EEG = pop_iclabel(EEG, 'default');
  140. if isfield(EEG,'etc') && isfield(EEG.etc,'ic_classification') && ...
  141. isfield(EEG.etc.ic_classification,'ICLabel')
  142. P = EEG.etc.ic_classification.ICLabel.classifications; % [IC x 7]
  143. % 列: Brain(1) Muscle(2) Eye(3) Heart(4) Line(5) Chan(6) Other(7)
  144. rm = find(P(:,2)>=thr | P(:,3)>=thr);
  145. if ~isempty(rm)
  146. fprintf(' Removing %d ICs (Eye/Muscle >= %.2f)\n', numel(rm), thr);
  147. EEG = pop_subcomp(EEG, rm, 0);
  148. else
  149. fprintf(' No IC meets Eye/Muscle >= %.2f\n', thr);
  150. end
  151. else
  152. warning('ICLabel result not found; skip removal.');
  153. end
  154. else
  155. warning('ICLabel plugin not found; skip IC removal.');
  156. end
  157. end
  159. function EEG = do_reref(EEG, mode)
  160. try
  161. if ischar(mode) && strcmpi(mode,'average')
  162. EEG = pop_reref(EEG, []);
  163. elseif iscell(mode)
  164. lab = {EEG.chanlocs.labels};
  165. idx = find(ismember(lab, mode));
  166. if isempty(idx), warning('Ref chans missing; using average.'); EEG = pop_reref(EEG, []);
  167. else, EEG = pop_reref(EEG, idx); end
  168. else
  169. EEG = pop_reref(EEG, []);
  170. end
  171. catch ME
  172. warning('Reref failed: %s; using average.', ME.message);
  173. EEG = pop_reref(EEG, []);
  174. end
  175. end
  177. function [segs_var, times_var, segs_1s, times_1s, info, keep_1s] = ...
  178. extract_premotor_segments(EEG, code_start, code_end, pre_ms, tol_ms_pair)
  179. % 输出：
  180. % segs_var/times_var : 55→14 variable length segment (0 alignment 14; time unit: second)
  181. % segs_1s/times_1s : Fixed 1s ([-1,0]s, 0 alignment 14)
  182. % info : Struct（i55/i14/t55/t14/dur_ms/valid）
  183. % keep_1s : Trial mask satisfying dur in [tol_lo, tol_hi]ms
  185. srate = EEG.srate;
  186. types = arrayfun(@(e) type2num(e.type), EEG.event);
  187. lats = [EEG.event.latency]; % sample point
  188. idx55 = find(types==code_start); % 55
  189. idx14 = find(types==code_end); % 14
  191. segs_var = {}; times_var = {};
  192. segs_1s = {}; times_1s = {};
  193. info = struct('i55',{},'i14',{},'t55',{},'t14',{},'dur_ms',{},'valid',{});
  194. keep_1s = [];
  196. pre_samp = round(pre_ms/1000 * srate);
  198. for ii = 1:numel(idx14)
  199. i14 = idx14(ii);
  200. t14 = lats(i14);
  202. % "The adjacent previous 55": the last 55 that comes after the previous 14 and before the current 14
  203. prev14_lat = -inf; if ii>1, prev14_lat = lats(idx14(ii-1)); end
  204. cand = idx55(lats(idx55) > prev14_lat & lats(idx55) < t14);
  205. i55 = NaN; t55 = NaN; valid = false; dur_ms = NaN;
  206. if ~isempty(cand)
  207. i55 = cand(end); t55 = lats(i55);
  208. dur_ms = (t14 - t55)/srate*1000;
  209. valid = (dur_ms>=0);
  210. end
  212. % variable length 55→14
  213. if valid
  214. s1 = max(1, round(t55));
  215. s2 = min(size(EEG.data,2), round(t14));
  216. segs_var{end+1} = EEG.data(:, s1:s2);
  217. t0 = ((s1:s2) - t14)/srate; % 0 aligh to 14
  218. times_var{end+1} = t0;
  219. else
  220. segs_var{end+1} = [];
  221. times_var{end+1} = [];
  222. end
  224. % Fixed 1s (only when dur is within tolerance)
  225. ok1s = valid && (dur_ms >= tol_ms_pair(1)) && (dur_ms <= tol_ms_pair(2));
  226. if ok1s
  227. s1f = round(t14 - pre_samp);
  228. s2f = round(t14);
  229. if s1f >= 1 && s2f <= size(EEG.data,2)
  230. segs_1s{end+1} = EEG.data(:, s1f:s2f);
  231. times_1s{end+1} = ((s1f:s2f) - t14)/srate;
  232. else
  233. segs_1s{end+1} = [];
  234. times_1s{end+1} = [];
  235. ok1s = false;
  236. end
  237. else
  238. segs_1s{end+1} = [];
  239. times_1s{end+1} = [];
  240. end
  242. info(end+1).i55 = i55; %#ok<AGROW>
  243. info(end ).i14 = i14;
  244. info(end ).t55 = t55;
  245. info(end ).t14 = t14;
  246. info(end ).dur_ms = dur_ms;
  247. info(end ).valid = valid;
  248. keep_1s(end+1,1) = ok1s; %#ok<AGROW>
  249. end
  250. end
  252. function v = type2num(x)
  253. if isnumeric(x), v = x; return; end
  254. if ischar(x) || isstring(x)
  255. y = str2double(x); if ~isnan(y), v = y; return; end
  256. end
  257. v = NaN;
  258. end
  260. function T = read_ball_labels(csvfile)
  261. Tb = readtable(csvfile);
  262. Tb.Properties.VariableNames = lower(strrep(strtrim(Tb.Properties.VariableNames),' ','_'));
  263. allnames = Tb.Properties.VariableNames;
  265. trial_col = pick_col(allnames, {'trial','trial_id','trialindex','trial_idx'});
  266. assert(~isempty(trial_col), 'CSV is missing the trial column');
  267. color_col = pick_col(allnames, {'color_ball','ball_color','color','colour'});
  268. assert(~isempty(color_col), 'CSV is missing color_ball / ball_color column');
  269. move_col = pick_col(allnames, {'move_direct','movedirect','move_direction','movement_direction','movement','direction','move_dir','dir'});
  270. assert(~isempty(move_col), 'CSV is missing move_direct column');
  272. % —— Cleaning: Remove leading and trailing quotation marks, white spaces, and convert to lowercase ——
  273. clean = @(s) regexprep(lower(strtrim(string(s))), '^["'']+|["'']+$', '');
  275. trial = Tb.(trial_col);
  276. color = clean(Tb.(color_col));
  277. move = clean(Tb.(move_col));
  279. u = unique(trial, 'stable');
  280. trial_csv = zeros(numel(u),1);
  281. color_lab = strings(numel(u),1);
  282. move_lab = strings(numel(u),1);
  283. label_code = -1*ones(numel(u),1,'int8');
  285. for k = 1:numel(u)
  286. m = (trial == u(k));
  287. c_k = char(mode(categorical(color(m))));
  288. d_k = char(mode(categorical(move(m))));
  289. color_lab(k) = string(c_k);
  290. move_lab(k) = string(d_k);
  291. trial_csv(k) = u(k);
  292. label_code(k)= map_label_code(d_k, c_k); % map to 0/1/2/3/-1
  293. end
  295. T = table(trial_csv, color_lab, move_lab, label_code, ...
  296. 'VariableNames', {'trial_csv','color_ball','move_direct','label_code'});
  298. function name = pick_col(names, aliases)
  299. name = '';
  300. for ii = 1:numel(aliases)
  301. hit = find(strcmpi(names, aliases{ii}), 1, 'first');
  302. if ~isempty(hit), name = names{hit}; return; end
  303. end
  304. end
  306. function code = map_label_code(move_dir, color_ball)
  307. md = clean(move_dir); cb = clean(color_ball);
  308. if md=="left" && cb=="red", code = int8(0); return; end
  309. if md=="right" && cb=="red", code = int8(1); return; end
  310. if md=="left" && cb=="yellow", code = int8(2); return; end
  311. if md=="right" && cb=="yellow", code = int8(3); return; end
  312. code = int8(-1);
  313. end
  314. end
  315. function [Sfix, Tfix] = force_len_fixed(Sin, Tin, target_len, nbchan, srate)
  316. % Unify each segment of EEG in the cell array into C×target_len:
  317. % - Trim at the end (T > target_len)
  318. % - Pad with 0s at the end (T<target_len)
  319. % Synchronized cropping/padding times (linear extension), in seconds
  320. n = numel(Sin);
  321. Sfix = cell(n,1);
  322. Tfix = cell(n,1);
  324. for i = 1:n
  325. Xi = Sin{i};
  326. ti = Tin{i};
  328. if isempty(Xi)
  329. Sfix{i} = zeros(nbchan, target_len);
  330. if ~isempty(srate) && isfinite(srate)
  331. dt = 1./double(srate);
  332. Tfix{i} = ((-target_len+1):0) * dt;
  333. else
  334. Tfix{i} = zeros(1, target_len);
  335. end
  336. continue;
  337. end
  339. [C,T] = size(Xi);
  340. if C ~= nbchan
  341. nb = C;
  342. else
  343. nb = nbchan;
  344. end
  346. % —— Data cropping/padding ——
  347. if T >= target_len
  348. Sfix{i} = Xi(:, 1:target_len);
  349. t_out = safe_times_trim(ti, target_len);
  350. else
  351. Xo = zeros(nb, target_len);
  352. Xo(:,1:T) = Xi;
  353. Sfix{i} = Xo;
  354. t_out = safe_times_pad(ti, target_len, srate);
  355. end
  356. Tfix{i} = t_out(:)';
  357. end
  358. end
  360. function t_out = safe_times_trim(t_in, target_len)
  361. if isempty(t_in)
  362. t_out = zeros(1, target_len);
  363. else
  364. t_in = t_in(:)';
  365. if numel(t_in) >= target_len
  366. t_out = t_in(1:target_len);
  367. else
  368. dt = median(diff(t_in));
  369. last = t_in(end);
  370. need = target_len - numel(t_in);
  371. t_extra = last + dt*(1:need);
  372. t_out = [t_in, t_extra];
  373. end
  374. end
  375. end
  377. function t_out = safe_times_pad(t_in, target_len, srate)
  378. if isempty(t_in)
  379. if ~isempty(srate) && isfinite(srate)
  380. dt = 1./double(srate);
  381. t_out = ((-target_len+1):0) * dt;
  382. else
  383. t_out = zeros(1, target_len);
  384. end
  385. return;
  386. end
  387. t_in = t_in(:)';
  388. T = numel(t_in);
  389. if T == 1
  390. dt = (1./double(srate));
  391. else
  392. dt = median(diff(t_in));
  393. if ~isfinite(dt) || dt<=0
  394. dt = (1./double(srate));
  395. end
  396. end
  397. need = target_len - T;
  398. if need <= 0
  399. t_out = t_in(1:target_len);
  400. else
  401. t_extra = t_in(end) + dt*(1:need);
  402. t_out = [t_in, t_extra];
  403. end
  404. end

preprocess_premove_55_14.m at commit c0b595d, under CC-BY-4.0 · at the source

Overview

Authors: Mengpu Cai1,2, Rongrong Fu1,3, Yaodong Wang4, Bin Lu1,2, Saiwei Guo1,2, Fangyao Xu1,2
  1. School of Electrical Engineering, Yanshan University, Qinhuangdao, 066004 China
  2. Hebei Key Laboratory of Measurement Technology and Instrumentation, Yanshan University, Qinhuangdao, 066000 Hebei Province China
  3. Key Laboratory of Intelligent Control and Neural Information Processing, Ministry of Education, Yanshan University, Qinhuangdao, 066004 Hebei Province China
  4. School of Computer Science, Wuhan University, Wuhan, 430072 China
Institutions: Yanshan University (China); Wuhan University (China)
Journal: Scientific data, volume 13, issue 1, article 933
Dates: received 31 October 2025; accepted 27 March 2026; published online 22 April 2026
Type: Data paper · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41597-026-07146-x · PMID 42020467 · PMCID PMC13287794 · OpenAlex W7155158592
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), other (modality), human (organism), methods / tools (subfield)
Methods: Spectral & time-frequency, Connectivity, Smoothing, state filtering, decompositions, Preprocessing, Machine learning
MeSH: Electroencephalography*, Hand*, Intention*, Biomechanical Phenomena, Humans, Movement (* major topic)
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: State Key Laboratory for Novel Software Technology, China (KFKT2025B88); National Natural Science Foundation of China (62073282); S & T Program of Qinhuangdao City, China (202302B015)
Citations: cited by 1 paper (Europe PMC); 24 references in the paper

Abstract

The Intention-Action Conflict EEG-Hand Kinematics Dataset (IACKD) is a joint resource for studying congruent and incongruent intention-action conditions during unimanual control. It comprises 7,040 trials from 15 participants. Each trial includes a 1-s pre-movement period and a movement-execution period. A target frame and a controllable ball define the task; ball color cues congruency (red = same direction; yellow = opposite). EEG was recorded with a 32-channel Compumedics Neuroscan system at 1024 Hz, and 3-D hand trajectories were captured with Leap Motion at 170 Hz. Streams are time-aligned, and complete preprocessing and alignment scripts are provided. Technical validation includes readiness potentials at C3/C4/Cz, μ/β-band ERD/ERS with post-movement β rebound, sub-30-ms cross-modal residuals on >99.375% of trials, and expected completion-time differences between congruent and incongruent conditions. IACKD is intended for reuse in intention decoding, continuous trajectory decoding, and evaluation of decoder robustness under conflict or perturbed feedback.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 15 matches between paragraphs and lines of code.

Boketto1/IACKD

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: c0b595de7571c7e04abae7477c61d0b92b5702cd, 28 January 2026
Languages: MATLAB (6), Jupyter (3), Python (2)
Size: 30 files, 11 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 3 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (5 files), NumPy (5 files), SciPy (5 files), EEGLAB (2 files), ICLabel (2 files), MNE-Python (2 files), pandas (2 files), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
12 files
At the source: github.com/Boketto1/IACKD

Code availability

The source code used for all technical validations conducted in this experiment has been uploaded and is publicly available on GitHub (https://github.com/Boketto1/IACKD).

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

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;
  • 11 scripts, each with its path and the digest of its content;
  • 15 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

Data availability

The dataset described in this study is publicly available on OpenNeuro under accession number ds006840 and can be accessed via its 10.18112/openneuro.ds006840.v1.0.0.

Reproduced under the paper's license (CC BY), from the paper cited above.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 6 MeSH terms, 3 funders, 17 references.

Cite

This paper

Cai, M., Fu, R., Wang, Y., Lu, B., Guo, S., & Xu, F. (2026). Intention-Action Conflict EEG-Hand Kinematics Dataset for Unimanual Control under Congruent and Incongruent Conditions. Scientific data, 13(1), 933. https://doi.org/10.1038/s41597-026-07146-x

BibTeX

@article{cai2026intention,
author = {Cai, Mengpu and Fu, Rongrong and Wang, Yaodong and Lu, Bin and Guo, Saiwei and Xu, Fangyao},
title = {{Intention-Action Conflict EEG-Hand Kinematics Dataset for Unimanual Control under Congruent and Incongruent Conditions}},
journal = {Scientific data},
year = {2026},
month = apr,
volume = {13},
number = {1},
pages = {933},
publisher = {Nature Publishing Group},
issn = {2052-4463},
doi = {10.1038/s41597-026-07146-x},
url = {https://doi.org/10.1038/s41597-026-07146-x},
pmid = {42020467},
pmcid = {PMC13287794}
}

RIS

TY - JOUR
AU - Cai, Mengpu
AU - Fu, Rongrong
AU - Wang, Yaodong
AU - Lu, Bin
AU - Guo, Saiwei
AU - Xu, Fangyao
TI - Intention-Action Conflict EEG-Hand Kinematics Dataset for Unimanual Control under Congruent and Incongruent Conditions
T2 - Scientific data
J2 - Sci Data
PY - 2026
DA - 2026/04/22
VL - 13
IS - 1
SP - 933
SN - 2052-4463
PB - Nature Publishing Group
DO - 10.1038/s41597-026-07146-x
UR - https://doi.org/10.1038/s41597-026-07146-x
LA - en
ER -

CSL-JSON

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"title": "Intention-Action Conflict EEG-Hand Kinematics Dataset for Unimanual Control under Congruent and Incongruent Conditions",
"container-title": "Scientific data",
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"given": "Mengpu"
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{
"family": "Fu",
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{
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"PMCID": "PMC13287794",
"ISSN": "2052-4463",
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"language": "en",
"issued": {
"date-parts": [
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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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