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Multidimensional dynamic characterization and decoding of finger movements using magnetoencephalography.

Code ↔ Paper

5 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 5 matches · 1 of them tie a paragraph to a whole file without given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Data processing › MEG and MRI data pre-processing ↔ Showresults/Figure4_MVPA_TimeCourse_Amap_Results.m, lines 6 to 24 · score 0.87 · 30–60 Hz, 0.5–90 Hz, 0.5–4 Hz, 13–30 Hz, 8–13 Hz, 60–90 Hz · Report a mismatch
  2. [2] § Methods › Data processing › MEG and MRI data pre-processing ↔ Showresults/SFigure4_Accuracy_PCA_AI_TFR_Results.m, lines 6 to 32 · score 0.87 · 30–60 Hz, 0.5–90 Hz, 0.5–4 Hz, 13–30 Hz, 8–13 Hz, 60–90 Hz · Report a mismatch
  3. [3] § Methods › Data acquisition ↔ func_wxyz/ft_headmovement_wxyz.m, lines 1 to 134 · score 0.75 · head position, MEG sensor, head movements, nasion, CTF, coils · Report a mismatch
  4. [4] § Methods › Data processing › EMG data processing ↔ func_wxyz/wxyz_emgdetect.m, the whole file · a weak match · score 0.68 · 20–450 Hz, band pass, rectified, threshold, raw, EMG · Report a mismatch
  5. [5] § Methods › Data processing › MEG and MRI data pre-processing ↔ func_wxyz/ft_headmovement_wxyz.m, lines 1 to 134 · score 0.61 · Head position, MEG sensor, coils, segmented, epochs, movement · Report a mismatch

Paper

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The authors' code

MATLAB · 418 lines · 16 KB · no license · 2 matches

  1. function [varargout] = ft_headmovement_wxyz(cfg,HLCdata)
  3. % Update: wxyz ROW 136
  5. % FT_HEADMOVEMENT outputs a raw data structure, or cell-array of data structures
  6. % reflecting the variability in the subject's head poisition relative to the
  7. % MEG sensors, based on continuous head position information. Current support is
  8. % only for CTF-data. The output timeseries contain the raw HLC data, and a
  9. % parametrization of the head movement in terms of translation and
  10. % rotations in 3D space. The grad structure(s) have head position information
  11. % incorporated in the coils' position/orientation and/or in the tra
  12. % matrix, depending on the method used.
  13. %
  14. % Use as
  15. % data = ft_headmovement(cfg)
  16. %
  17. % where the configuration should contain
  18. % cfg.dataset = string with the filename
  19. % cfg.method = string, 'updatesens' (default), 'cluster', 'avgoverrpt',
  20. % 'pertrial_cluster', 'pertrial' (default = 'updatesens')
  21. %
  22. % optional arguments are
  23. % cfg.trl = empty (default), or Nx3 matrix with the trial
  24. % definition (see FT_DEFINETRIAL). When specified as empty,
  25. % the whole recording is used.
  26. % cfg.numclusters = number of segments with constant headposition in
  27. % which to split the data (default = 10). This argument
  28. % is only used for the methods that use clustering ('updatesens',
  29. % 'cluster', 'pertrial_cluster').
  30. %
  31. % If cfg.method = 'updatesens', the grad in the single output structure has
  32. % a specification of the coils expanded as per the centroids of the position
  33. % clusters (obtained by kmeans clustering of the HLC time series). The balancing matrix
  34. % is a weighted concatenation of the original tra-matrix. This method requires
  35. % cfg.numclusters to be specified
  36. %
  37. % If cfg.method = 'avgoverrpt', the grad in the single output structure has
  38. % a specification of the coils according to the average head position
  39. % across the specified samples.
  40. %
  41. % If cfg.method = 'cluster', the cell-array of output structures represent
  42. % the epochs in which the head was considered to be positioned close to the
  43. % corresponding kmeans-cluster's centroid. The corresponding grad-structure
  44. % is specified according to this cluster's centroid. This method requires
  45. % cfg.numclusters to be specified.
  46. %
  47. % If cfg.method = 'pertrial', the cell-array of output structures contains
  48. % single trials, each trial with a trial-specific grad structure. Note that
  49. % this is extremely memory inefficient with large numbers of trials, and
  50. % probably an overkill.
  51. %
  52. % If cfg.method = 'pertrial_clusters', the cell-array of output structures
  53. % contains sets of trials where the trial-specific head position was
  54. % considered to be positioned close to the corresponding kmeans-cluster's
  55. % centroid. The corresponding grad-structure is specified accordin to the
  56. % cluster's centroid. This method requires cfg.numclusters to be specified.
  57. %
  58. % The updatesens method and related methods are described by Stolk et al., Online and
  59. % offline tools for head movement compensation in MEG. NeuroImage, 2012.
  60. %
  61. % See also FT_REGRESSCONFOUND, FT_REALTIME_HEADLOCALIZER
  63. % Copyright (C) 2008-2018, Jan-Mathijs Schoffelen, Robert Oostenveld
  64. %
  65. % This file is part of FieldTrip, see http://www.fieldtriptoolbox.org
  66. % for the documentation and details.
  67. %
  68. % FieldTrip is free software: you can redistribute it and/or modify
  69. % it under the terms of the GNU General Public License as published by
  70. % the Free Software Foundation, either version 3 of the License, or
  71. % (at your option) any later version.
  72. %
  73. % FieldTrip is distributed in the hope that it will be useful,
  74. % but WITHOUT ANY WARRANTY; without even the implied warranty of
  75. % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
  76. % GNU General Public License for more details.
  77. %
  78. % You should have received a copy of the GNU General Public License
  79. % along with FieldTrip. If not, see <http://www.gnu.org/licenses/>.
  80. %
  81. % $Id$
  83. % these are used by the ft_preamble/ft_postamble function and scripts
  84. ft_revision = '$Id$';
  85. ft_nargin = nargin;
  86. ft_nargout = nargout;
  88. % do the general setup of the function
  89. ft_defaults
  90. ft_preamble init
  91. ft_preamble debug
  92. ft_preamble provenance
  94. % the ft_abort variable is set to true or false in ft_preamble_init
  95. if ft_abort
  96. return
  97. end
  99. % check if the input cfg is valid for this function
  100. % FIXME: consider allowing a data structure as input.
  101. cfg = ft_checkconfig(cfg, 'dataset2files', 'yes');
  103. % set the defaults
  104. cfg.method = ft_getopt(cfg, 'method', 'updatesens'); % 'pertrial', 'pertrial_cluster', 'avgoverrpt', 'cluster'
  105. cfg.numclusters = ft_getopt(cfg, 'numclusters', 10);
  106. cfg.feedback = ft_getopt(cfg, 'feedback', 'yes');
  108. if isequal(cfg.method,'updatesens') || isequal(cfg.method, 'pertrial_cluster') || isequal(cfg.method, 'cluster')
  109. dokmeans = true;
  110. else
  111. dokmeans = false;
  112. end
  114. % read the header information and check whether it's a CTF dataset with HLC information
  115. hdr = ft_read_header(cfg.headerfile);
  116. assert(startsWith(ft_senstype(hdr), 'ctf'), 'currently only CTF MEG data is supported');
  117. assert(numel(intersect(hdr.label, {'HLC0011' 'HLC0012' 'HLC0013' 'HLC0021' 'HLC0022' 'HLC0023' 'HLC0031' 'HLC0032' 'HLC0033'}))==9, 'the data does not contain the expected head localizer channels');
  119. grad_head = ft_datatype_sens(ctf2grad(hdr.orig, 0));
  120. grad_dewar = ft_datatype_sens(ctf2grad(hdr.orig, 1));
  122. grad = grad_dewar; % we work with dewar coordinates, ...
  123. grad.chanpos = grad_head.chanpos;
  125. % read the HLC-channels
  126. % HLC0011 HLC0012 HLC0013 x, y, z coordinates of nasion-coil in m.
  127. % HLC0021 HLC0022 HLC0023 x, y, z coordinates of lpa-coil in m.
  128. % HLC0031 HLC0032 HLC0033 x, y, z coordinates of rpa-coil in m.
  130. % raw script read HLC-channels
  131. % tmpcfg = keepfields(cfg, {'datafile' 'trl'});
  132. % tmpcfg.channel = {'HLC0011' 'HLC0012' 'HLC0013' 'HLC0021' 'HLC0022' 'HLC0023' 'HLC0031' 'HLC0032' 'HLC0033'};
  133. % tmpcfg.continuous = 'yes';
  134. % data = ft_preprocessing(tmpcfg);
  136. %% wxyz - data can be passed by the user, but need cfg.dataset to read CTF.hdr file
  137. data = HLCdata;
  139. if ~isfield(cfg, 'trl') || isempty(cfg.trl)
  140. cfg.trl = [1 hdr.nTrials.*hdr.nSamples 0];
  141. end
  143. %%
  144. data = removefields(data, 'elec'); % this slows down a great deal
  145. % rendering the persistent variable trick useless. We don't need the elec anyway
  147. wdat = cellfun('size', data.time, 2); % weights for weighted average
  149. trial_index = cell(1,numel(data.trial));
  150. for k = 1:numel(data.trial)
  151. % it sometimes happens that data are numerically 0, which causes problems downstream, replace with nans
  152. data.trial{k}(:,sum(data.trial{k}==0)==9) = nan;
  154. % create a bookkeeping cell-array, indexing the trial-indx
  155. trial_index{k} = k.*ones(1,numel(data.time{k}));
  156. end
  158. % average across time if needed
  159. if isequal(cfg.method, 'pertrial') || isequal(cfg.method, 'avgoverrpt') || isequal(cfg.method, 'pertrial_cluster')
  160. tmpcfg = [];
  161. tmpcfg.avgovertime = 'yes';
  162. tmpcfg.nanmean = 'yes';
  163. data_timeavg = ft_selectdata(tmpcfg, data);
  165. % concatenate across trials and scale the units
  166. dat = cat(2, data_timeavg.trial{:});
  167. else
  168. % concatenate across trials and scale the units
  169. dat = cat(2, data.trial{:});
  170. end
  172. % scale in units of the gradiometer definition, which is probably cm
  173. dat = dat * ft_scalingfactor('m', grad.unit);
  175. if isequal(cfg.method, 'pertrial_cluster')
  176. trl_idx = 1:numel(data.trial);
  177. else
  178. trl_idx = cat(2, trial_index{:});
  179. end
  181. % average across trials if needed
  182. if isequal(cfg.method, 'avgoverrpt')
  183. dat = sum(dat*diag(wdat), 2)./sum(wdat);
  184. end
  186. % remove duplicates if clustering is to be performed
  187. if dokmeans && ~isequal(cfg.method, 'pertrial_cluster')
  188. [tmpdata, dum, ic] = unique(dat', 'rows');
  189. dat = tmpdata';
  191. % count how often each position occurs
  192. wdat = hist(ic, unique(ic));
  193. end
  195. % perform the clustering if needed
  196. if dokmeans
  197. % compute the cluster means
  198. [bin, dat] = kmeans(dat', cfg.numclusters, 'EmptyAction', 'drop');
  200. % create a cell-array 1xnrpt with time specific indices of cluster id
  201. cluster_id = cell(1,numel(data.trial));
  202. for k = 1:numel(data.trial)
  203. cluster_id{k} = nan+zeros(1,numel(data.time{k}));
  204. if ~isequal(cfg.method, 'pertrial_cluster')
  205. for m = 1:size(dat,1)
  206. tmpdat = ic(trl_idx==k);
  207. cluster_id{k}(ismember(tmpdat, find(bin==m))) = m;
  208. end
  209. else
  210. cluster_id{k}(:) = bin(k);
  211. end
  212. end
  214. else
  215. bin = 1:size(dat,2);
  216. dat = dat';
  217. end
  219. % find the three channels for each head localizer coil
  220. selnas = match_str(data.label,{'HLC0011';'HLC0012';'HLC0013'});
  221. sellpa = match_str(data.label,{'HLC0021';'HLC0022';'HLC0023'});
  222. selrpa = match_str(data.label,{'HLC0031';'HLC0032';'HLC0033'});
  224. ubin = unique(bin(isfinite(bin)));
  225. nas = zeros(numel(ubin),3);
  226. lpa = zeros(numel(ubin),3);
  227. rpa = zeros(numel(ubin),3);
  228. numperbin = zeros(numel(ubin),1);
  229. for k = 1:length(ubin)
  230. nas(k, :) = dat(k, selnas);
  231. lpa(k, :) = dat(k, sellpa);
  232. rpa(k, :) = dat(k, selrpa);
  233. numperbin(k) = sum(wdat(bin==ubin(k)));
  234. end
  236. hc = read_ctf_hc([cfg.datafile(1:end-4),'hc']);
  238. if istrue(cfg.feedback)
  239. % plot some stuff
  240. figure; hold on;
  241. title(sprintf('%s coordinates (%s)', grad_dewar.coordsys, grad_dewar.unit));
  242. ft_plot_axes(grad_dewar);
  243. ft_plot_sens(grad_dewar);
  245. fiducials = [nas;lpa;rpa];
  246. plot3(fiducials(:,1), fiducials(:,2), fiducials(:,3), 'b.');
  247. plot3(hc.dewar.nas(1), hc.dewar.nas(2), hc.dewar.nas(3), 'ro');
  248. plot3(hc.dewar.lpa(1), hc.dewar.lpa(2), hc.dewar.lpa(3), 'ro');
  249. plot3(hc.dewar.rpa(1), hc.dewar.rpa(2), hc.dewar.rpa(3), 'ro');
  250. axis vis3d; axis off
  251. end
  253. % compute transformation matrix from dewar to head coordinates
  254. dewar2head = zeros(4, 4, size(nas,1));
  255. for k = 1:size(dewar2head, 3)
  256. dewar2head(:,:,k) = ft_headcoordinates(nas(k,:), lpa(k,:), rpa(k,:), 'ctf');
  257. end
  259. if isequal(cfg.method, 'updatesens')
  260. npos = size(dewar2head, 3);
  261. ncoils = size(grad.coilpos, 1);
  262. gradnew = grad;
  263. gradnew.coilpos = zeros(size(grad.coilpos,1)*npos, size(grad.coilpos,2));
  264. gradnew.coilori = zeros(size(grad.coilpos,1)*npos, size(grad.coilpos,2));
  265. gradnew.tra = repmat(grad.tra, [1 npos]);
  266. for m = 1:npos
  267. tmptransform = dewar2head(:,:,m);
  268. gradnew.coilpos((m-1)*ncoils+1:(m*ncoils), :) = ft_warp_apply(tmptransform, grad.coilpos); % back to head coordinates
  269. tmptransform(1:3, 4) = 0; % keep only the rotation
  270. gradnew.coilori((m-1)*ncoils+1:(m*ncoils), :) = ft_warp_apply(tmptransform, grad.coilori);
  271. gradnew.tra(:, (m-1)*ncoils+1:(m*ncoils)) = grad.tra.*(numperbin(m)./sum(numperbin));
  272. end
  273. grad = gradnew;
  274. else
  275. npos = size(dewar2head, 3);
  276. for k = 1:npos
  277. grad(k) = ft_transform_geometry(dewar2head(:,:,k), grad_dewar);
  278. end
  279. end
  281. % prepare the output data
  282. switch cfg.method
  283. case 'cluster'
  284. varargout = cell(1,numel(grad));
  285. tmpdata = data;
  286. tmpdata.trial = cluster_id;
  287. tmpdata.label = {'cluster_id'};
  288. data = ft_appenddata([],data,tmpdata);
  290. for k = 1:numel(grad)
  291. tmpcfg = [];
  292. tmpcfg.artfctdef.bpfilter = 'no';
  293. tmpcfg.artfctdef.threshold.channel = {'cluster_id'};
  294. tmpcfg.artfctdef.threshold.min = 0.9+k-1;
  295. tmpcfg.artfctdef.threshold.max = 1.1+k-1;
  296. tmpcfg.artfctdef.threshold.bpfilter = 'no';
  297. tmpcfg = ft_artifact_threshold(tmpcfg, tmpdata);
  298. artifacts = tmpcfg.artfctdef.threshold.artifact;
  300. tmpcfg = [];
  301. tmpcfg.artfctdef.reject = 'partial';
  302. tmpcfg.artfctdef.threshold.artifact = artifacts;
  303. tmpdata_clus = ft_rejectartifact(tmpcfg, data);
  304. tmpdata_clus.grad = grad(k);
  306. varargout{k} = tmpdata_clus;
  307. end
  309. case {'avgoverrpt' 'updatesens'}
  310. data.grad = grad;
  311. varargout{1} = data;
  313. case 'pertrial'
  314. ft_error('not yet implemented');
  316. case 'pertrial_cluster'
  317. varargout = cell(1,numel(grad));
  318. tmpdata = data;
  319. tmpdata.trial = cluster_id;
  320. tmpdata.label = {'cluster_id'};
  321. data = ft_appenddata([],data,tmpdata);
  322. for k = 1:numel(grad)
  323. tmpcfg = [];
  324. tmpcfg.trials = find(bin==k);
  325. tmpdata_clus = ft_selectdata(tmpcfg, data);
  326. %tmpcfg.previous = tmpdata_clus.cfg;
  328. %tmpdata_clus.cfg = tmpcfg;
  329. tmpdata_clus.grad = grad(k);
  331. varargout{k} = tmpdata_clus;
  332. end
  333. end % switch method
  335. for k = 1:numel(varargout)
  336. for m = 1:numel(varargout{k}.trial)
  337. nas = varargout{k}.trial{m}(selnas,:);
  338. lpa = varargout{k}.trial{m}(sellpa,:);
  339. rpa = varargout{k}.trial{m}(selrpa,:);
  340. cc = circumcenter(nas, lpa, rpa);
  341. varargout{k}.trial{m} = cat(1, varargout{k}.trial{m}, cc);
  342. end
  343. varargout{k}.label = cat(1, varargout{k}.label, {'cc_xpos';'cc_ypos';'cc_zpos';'cc_xrot';'cc_yrot';'cc_zrot'});
  344. end
  346. % do the general cleanup and bookkeeping at the end of the function
  347. ft_postamble debug
  348. ft_postamble provenance
  349. ft_postamble previous varargout
  350. ft_postamble history varargout
  351. ft_postamble savevar varargout
  353. function [cc] = circumcenter(coil1, coil2, coil3)
  355. % CIRCUMCENTER determines the position and orientation of the circumcenter
  356. % of the three fiducial markers (MEG headposition coils).
  357. %
  358. % Input: X,y,z-coordinates of the 3 coils [3 X N],[3 X N],[3 X N] where N
  359. % is timesamples/trials.
  360. %
  361. % Output: X,y,z-coordinates of the circumcenter [1-3 X N], and the
  362. % orientations to the x,y,z-axes [4-6 X N].
  363. %
  364. % A. Stolk, 2012
  366. % number of timesamples/trials
  367. N = size(coil1,2);
  369. % x-, y-, and z-coordinates of the circumcenter: use coordinates relative to point 'a' of the triangle
  370. xba = coil2(1,:) - coil1(1,:);
  371. yba = coil2(2,:) - coil1(2,:);
  372. zba = coil2(3,:) - coil1(3,:);
  373. xca = coil3(1,:) - coil1(1,:);
  374. yca = coil3(2,:) - coil1(2,:);
  375. zca = coil3(3,:) - coil1(3,:);
  377. % squares of lengths of the edges incident to 'a'
  378. balength = xba .* xba + yba .* yba + zba .* zba;
  379. calength = xca .* xca + yca .* yca + zca .* zca;
  381. % cross product of these edges
  382. xcrossbc = yba .* zca - yca .* zba;
  383. ycrossbc = zba .* xca - zca .* xba;
  384. zcrossbc = xba .* yca - xca .* yba;
  386. % calculate the denominator of the formulae
  387. denominator = 0.5 ./ (xcrossbc .* xcrossbc + ycrossbc .* ycrossbc + zcrossbc .* zcrossbc);
  389. % calculate offset (from 'a') of circumcenter
  390. xcirca = ((balength .* yca - calength .* yba) .* zcrossbc - (balength .* zca - calength .* zba) .* ycrossbc) .* denominator;
  391. ycirca = ((balength .* zca - calength .* zba) .* xcrossbc - (balength .* xca - calength .* xba) .* zcrossbc) .* denominator;
  392. zcirca = ((balength .* xca - calength .* xba) .* ycrossbc - (balength .* yca - calength .* yba) .* xcrossbc) .* denominator;
  394. % add the offset back to get the position of the origin over time.
  395. cc(1,:) = xcirca + coil1(1,:);
  396. cc(2,:) = ycirca + coil1(2,:);
  397. cc(3,:) = zcirca + coil1(3,:);
  399. % orientation of the circumcenter with respect to the x-, y-, and z-axis coordinates
  400. v = [cc(1,:)', cc(2,:)', cc(3,:)' ];
  401. vx = [zeros(1,N)', cc(2,:)', cc(3,:)' ]; % on the x-axis
  402. vy = [cc(1,:)', zeros(1,N)', cc(3,:)' ]; % on the y-axis
  403. vz = [cc(1,:)', cc(2,:)', zeros(1,N)']; % on the z-axis
  405. thetax = zeros(1,N);
  406. thetay = zeros(1,N);
  407. thetaz = zeros(1,N);
  408. for j = 1:N
  409. % find the angles of two vectors opposing the axes
  410. thetax(j) = acos(dot(v(j,:),vx(j,:))/(norm(v(j,:))*norm(vx(j,:))));
  411. thetay(j) = acos(dot(v(j,:),vy(j,:))/(norm(v(j,:))*norm(vy(j,:))));
  412. thetaz(j) = acos(dot(v(j,:),vz(j,:))/(norm(v(j,:))*norm(vz(j,:))));
  414. % convert to degrees
  415. cc(4,j) = (thetax(j) * (180/pi));
  416. cc(5,j) = (thetay(j) * (180/pi));
  417. cc(6,j) = (thetaz(j) * (180/pi));
  418. end

ft_headmovement_wxyz.m at commit 41f58cb, no license · at the source

Overview

Authors: Yu Zheng1,2, Xu Wang1,3, Li Zheng1, Hongtao Zhang1, Fan Wang1,2, Yan Zhuo1,2
ORCID iDs: Yu Zheng, Xu Wang, Li Zheng, Hongtao Zhang, Fan Wang, Yan Zhuo
  1. State Key Laboratory of Cognitive Science and Mental Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
  2. University of Chinese Academy of Sciences, Beijing, China
  3. State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
Institutions: Chinese Academy of Sciences (China); Institute of Biophysics (China); University of Chinese Academy of Sciences (China); Shanghai Jiao Tong University (China); State Key Laboratory of Mechanical System and Vibration (China)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1182
Dates: received 30 May 2025; accepted 2 March 2026; published online 30 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1182 · PMID 41924028 · PMCID PMC13037661 · OpenAlex W7134854990
Open access: diamond, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: MEG (modality), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, fMRI & imaging, Physiology & signal measures
Keywords: magnetoencephalography, finger movement, source level, motor execution
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (82402500, U21A20388); Chinese Academy of Sciences; Ministry of Science and Technology of the People's Republic of China (2022ZD0211901); Institute of Biophysics, Chinese Academy of Sciences
Citations: not cited yet (Europe PMC); 71 references in the paper

Abstract

The similarity of neural activity in finger movements poses challenges for accurate decoding using many non-invasive imaging techniques. Magnetoencephalography (MEG), with its relatively high spatial resolution, offers the potential to capture the underlying dynamic neural differences. In this study, we recorded MEG signals during single extension movements of the right-hand fingers, examining the time-varying cortical activation patterns across different frequency bands and their contribution to decoding finger movements. Our results demonstrate that signals below 8 Hz not only enable effective movement classification but also reveal millisecond-scale neural activation patterns in the sensorimotor cortex. Furthermore, incorporating the spatiotemporal dynamics of neural activity may enhance decoding performance for fine motor control. These findings highlight the value of integrating temporal, frequency, and spatial dimensions in studying motor neural activity and underscore MEG’s potential for broader applications in movement-related neurophysiology and brain–computer interface research.

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 5 matches between paragraphs and lines of code.

Yzheng9725/MEG-study-fingermovement

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 41f58cb75aa0e7cb80caee7e305d3c09176392eb, 22 January 2026
Languages: MATLAB (49)
Size: 77 files, 49 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README, tests, documentation
Not found: license file, CITATION.cff, environment file, continuous integration
Tools: FieldTrip (30 files), Statistics and Machine Learning Toolbox (4 files), boundedline (3 files), GIfTI library for MATLAB (3 files), Violinplot-Matlab (3 files), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
50 files
At the source: github.com/Yzheng9725/MEG-study-fingermovement

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

Tracing map

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  • 49 scripts, each with its path and the digest of its content;
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Data

Datasets cited

Data and Code Availability

The group-level data files to generate the figures are available at https://doi.org/10.6084/m9.figshare.28623506. The raw MEG and MRI data cannot be available according to the ethical permission. The custom code used for the analysis pipeline and results visualization is available at https://github.com/Yzheng9725/MEG-study-fingermovement.

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

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 4 keywords, 4 funders, 70 references.

Cite

This paper

Zheng, Y., Wang, X., Zheng, L., Zhang, H., Wang, F., & Zhuo, Y. (2026). Multidimensional dynamic characterization and decoding of finger movements using magnetoencephalography. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1182. https://doi.org/10.1162/imag.a.1182

BibTeX

@article{zheng2026multidimensional,
author = {Zheng, Yu and Wang, Xu and Zheng, Li and Zhang, Hongtao and Wang, Fan and Zhuo, Yan},
title = {{Multidimensional dynamic characterization and decoding of finger movements using magnetoencephalography}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = mar,
volume = {4},
pages = {IMAG.a.1182},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1182},
url = {https://doi.org/10.1162/imag.a.1182},
pmid = {41924028},
pmcid = {PMC13037661}
}

RIS

TY - JOUR
AU - Zheng, Yu
AU - Wang, Xu
AU - Zheng, Li
AU - Zhang, Hongtao
AU - Wang, Fan
AU - Zhuo, Yan
TI - Multidimensional dynamic characterization and decoding of finger movements using magnetoencephalography
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/03/30
VL - 4
SP - IMAG.a.1182
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1182
UR - https://doi.org/10.1162/imag.a.1182
LA - en
ER -

CSL-JSON

{
"id": "10.1162/imag.a.1182",
"type": "article-journal",
"title": "Multidimensional dynamic characterization and decoding of finger movements using magnetoencephalography",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
{
"family": "Zheng",
"given": "Yu"
},
{
"family": "Wang",
"given": "Xu"
},
{
"family": "Zheng",
"given": "Li"
},
{
"family": "Zhang",
"given": "Hongtao"
},
{
"family": "Wang",
"given": "Fan"
},
{
"family": "Zhuo",
"given": "Yan"
}
],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1182",
"DOI": "10.1162/imag.a.1182",
"PMID": "41924028",
"PMCID": "PMC13037661",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1182",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
30
]
]
}
}

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