OSCR

Sustained Attention Task (gradCPT) Dataset using simultaneous EEG-fMRI and DTI.

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
  1. [1] § Methods › DWI › Structural connectivity ↔ scripts/dwi/Structural_Connectivity.sh, lines 66–154 · score 0.89 · Probabilistic tractography, seed2all_length_roi, structural connectivity, PROBTRACKX2, BEDPOSTX, ROIs
  2. [2] § Methods › DWI › Structural connectivity ↔ scripts/dwi/Structural_Connectivity.sh, lines 66–154 · score 0.88 · Desikan Killiany, DWI space, FreeSurfer, T1 space, structural connectivity, atlas
  3. [3] § Methods › MRI preprocessing ↔ fmri/preprocessing/ccs_bids_04_funcnuisance.sh, lines 72–158 · score 0.80 · nuisance regression, global signal, motion parameters, components, derivatives, CSF
  4. [4] § Methods › DWI preprocessing ↔ scripts/dwi/MRtrix_DWI_Preproc.sh, lines 22–110 · score 0.79 · distortion correction, MRtrix, dwibiascorrect, dwidenoise, dwifslpreproc, eddy
  5. [5] § Technical Validation › fMRI: Measure for sustained attention lapse ↔ experiement_code folder/ CPT_withinSubject1_v2.m, lines 802–875 · score 0.79 · commission errors, omission errors, correct commissions, correct omissions, rejection, city
  6. [6] § Methods › DWI › Structural connectivity ↔ scripts/dwi/connectome_csv.m, lines 35–93 · score 0.77 · seed2all_length_roi, streamline lengths, symmetric, atlas, Probabilistic, Connectivity
  7. [7] § Methods › DWI › Structural connectivity ↔ scripts/dwi/connectome_csv.m, lines 35–93 · score 0.77 · connection probability, seed2all length, Streamline lengths, symmetrized, ROI, voxel
  8. [8] § Technical Validation › fMRI: Measure for sustained attention lapse ↔ experiement_code folder/ CPT_withinSubject1_v2.m, lines 802–875 · score 0.70 · Omission error, commission error, correct commissions, correct omissions, CPT
  9. [9] § Technical Validation › Limitations and technical challenges ↔ eeg/natview_eeg_preprocess_pipeline.m, lines 121–175 · score 0.69 · QRS detection, ECG channel, EEG channels, EEGLAB, FMRIB, median
  10. [10] § Methods › DWI preprocessing ↔ scripts/dwi/MRtrix_DWI_Preproc.sh, lines 22–110 · score 0.69 · fiber orientation, MRtrix, tractography, denoising, streamline, bias
  11. [11] § Methods › MRI preprocessing ↔ fmri/ccs_preproc_bids_docker.sh, lines 102–153 · score 0.63 · nuisance regression, FWHM, BIDS, smoothed, slice, pipeline
  12. [12] § Methods › EEG preprocessing ↔ eeg/natview_eeg_preprocess_pipeline.m, lines 121–175 · score 0.63 · QRS complexes, ECG channel, detection, EEGLAB, median, preprocessing
  13. [13] § Methods › EEG preprocessing ↔ eeg/natview_eeg_preprocess_pipeline.m, lines 177–187 · score 0.60 · pulse artifacts, artifact removal, QRS, FMRIB, template, preprocessing
  14. [14] § Methods › Gradient artifact removal ↔ eeg/natview_eeg_preprocess_pipeline.m, lines 96–117 · score 0.54 · remove gradient artifacts, FASTR, FMRIB, removal, EEG
  15. [15] § Technical Validation › fMRI data correlations ↔ fmri/preprocessing/ccs_bids_04_funcnuisance.sh, lines 72–158 · score 0.52 · temporal derivative, motion parameters, signal, fMRI

Paper

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The authors' code

MATLAB · 303 lines · 10 KB · CC-BY-SA-4.0 · 4 matches

  1. function EEG = natview_eeg_preprocess_pipeline(fileNameSET,outputDir,saveIntermediates,options)
  2. %% PURPOSE: This script preprocesses EEG data using EEGLAB functions
  3. % Simultaneously EEG and fMRI data was collected for the
  4. % NATVIEW_EEGFMRI dataset in a Siemens TrioTim 3T MRI scanner and
  5. % with Brain Products BrainCap MR at the Nathan Kline Institute
  6. % in Orangeburg, NY.
  7. %
  8. % This script utilizes the following toolboxes/plugins:
  9. %
  10. % EEGLAB (https://sccn.ucsd.edu/eeglab/index.php)
  11. % FMRIB Plug-In (https://fsl.fmrib.ox.ac.uk/eeglab/fmribplugin/)
  12. %
  13. % The toolbox and plugins above remove various artificats
  14. % (gradient artifact, pulse artifact, etc.) and performs other
  15. % preprocessing steps to prepare EEG data for secondary analysis.
  16. %
  17. % NOTE: This function uses the SET file format for file input
  18. %
  19. %--------------------------------------------------------------------------
  20. % INPUT:
  21. % fileNameSET - Filename of SET file
  22. %
  23. % outputDir - Output directory for preprocessed EEG file(s)
  24. %
  25. % saveIntermediates - Flag to save intermediate preprocessing steps
  26. % (Default: 0)
  27. %
  28. % options - Input STRUCT for saving specific intermediate steps
  29. % (Default: options.final = 1)
  30. %
  31. %--------------------------------------------------------------------------
  32. %% Error Checking
  33. % Output directory (Default: current working directory)
  34. if(nargin < 2 || isempty(outputDir))
  35. outputDir = pwd;
  36. end
  38. % Save intermediate preprocessing files (Default: 0)
  39. if(nargin < 3 || isempty(saveIntermediates))
  40. saveIntermediates = 0;
  41. end
  43. % Options STRUCT (Default: Only save final output)
  44. % NOTE: User can edit default flags here to save specific intermediate steps
  45. if(nargin < 4 || isempty(options))
  46. options.step1_gradient = 0;
  47. options.step2a_qrs = 0;
  48. options.step2b_pulse = 0;
  49. options.step3_downsample = 0;
  50. options.step4_nonEEG = 0;
  51. options.step5_bandpass = 0;
  52. options.step6_bad = 0;
  53. options.step7_asr = 0;
  54. options.step8_reference = 0;
  55. options.step9_ica = 0;
  56. options.final = 1;
  57. end
  59. % Create 'final' flag if not included with input options
  60. if(~isfield(options,'final'))
  61. options.final = 1;
  62. end
  64. %% STEP 0: Load data into EEGLAB
  65. [~,fileName] = fileparts(fileNameSET);
  66. EEG = pop_loadset(fileNameSET); % Load SET file into MATLAB
  67. EEG.data = double(EEG.data);
  69. underscore_idx = strfind(fileName,'_');
  70. fileInfo = cell(length(underscore_idx),1);
  71. for ii = 1:length(underscore_idx)
  72. if(ii==1)
  73. fileInfo{ii} = fileName(1:underscore_idx(1)-1);
  74. else
  75. fileInfo{ii} = fileName(underscore_idx(ii-1)+1:underscore_idx(ii)-1);
  76. end
  77. end
  79. subject = fileInfo{1}; % Participant ID
  80. session = fileInfo{2}; % Session
  81. task = fileInfo{3}; % Task Name
  83. if(length(fileInfo) > 3)
  84. runNum = fileInfo{4}; % Run
  85. output_fileName = [subject,'_',session,'_',task,'_',runNum];
  86. else
  87. output_fileName = [subject,'_',session,'_',task];
  88. end
  90. %% Non-EEG Channel specification
  91. ECGChan = find(strcmp({EEG.chanlocs.labels},'ECG'));
  92. EOGLChan = find(strcmp({EEG.chanlocs.labels},'EOGL'));
  93. EOGUChan = find(strcmp({EEG.chanlocs.labels},'EOGU'));
  94. electrodeExclude = [ECGChan,EOGLChan,EOGUChan];
  96. %% STEP 1: Gradient Artifact Removal
  97. % This step performs gradient artifact removal using FMRIB Toolbox
  98. % Link: https://fsl.fmrib.ox.ac.uk/eeglab/fmribplugin/
  99. if(strcmp(task,'task-checker') || ...
  100. strcmp(task,'task-dme') || ...
  101. strcmp(task,'task-dmh') || ...
  102. strcmp(task,'task-inscapes') || ...
  103. strcmp(task,'task-monkey1') || ...
  104. strcmp(task,'task-monkey2') || ...
  105. strcmp(task,'task-monkey5') || ...
  106. strcmp(task,'task-peer') || ...
  107. strcmp(task,'task-rest') || ...
  108. strcmp(task,'task-tp'))
  109. EEG = pop_fmrib_fastr(EEG,[],[],[],'R128',1,0,[],[],[],[],electrodeExclude,'auto'); % Remove gradient artifact
  111. % Save intermediate
  112. if(saveIntermediates == 1 && isfield(options, 'step1_gradient'))
  113. if(options.step1_gradient == 1)
  114. pop_saveset(EEG,'filename',[output_fileName,'_preprocess-1gradient_eeg'],'filepath',outputDir);
  115. end
  116. end
  117. end
  121. %% STEP 2a: QRS Detection
  122. % This step detects QRS complexes in the ECG channel. If the function fails
  123. % to find QRS complexes, QRS detection is performed on every EEG channel;
  124. % the channel chosen for QRS detection equals the mode of QRS counts
  125. try
  126. EEG = pop_fmrib_qrsdetect(EEG,ECGChan,'QRS','no'); % FMRIB Toolbox QRS Detection
  127. catch
  128. nChannels = EEG.nbchan;
  129. channelEEG = 1:nChannels;
  131. QRSCount = zeros(nChannels,1);
  132. channelError = zeros(nChannels,1);
  134. for nn = 1:nChannels
  135. try
  136. EEG_QRS = pop_fmrib_qrsdetect(EEG,channelEEG(nn),'QRS','no');
  137. eventLatency = extract_eventLatency(EEG_QRS,'QRS');
  139. if(length(eventLatency) > (EEG.xmax - 50) || nn ~= 32)
  140. QRSCount(nn) = length(eventLatency);
  141. end
  142. catch
  143. channelError(nn) = 1;
  144. end
  145. end
  147. channelEEG(QRSCount == 0) = [];
  148. QRSCount(QRSCount == 0) = [];
  150. [QRSCount_mode, QRSCount_modeNum] = mode(QRSCount);
  151. [QRSCount_sort, QRSCount_sort_idx] = sort(QRSCount);
  153. % Select mode of QRS count if 3 or more, else select median QRS count
  154. if(QRSCount_modeNum >= 3)
  155. QRSCount_mode_idx = find(QRSCount == QRSCount_mode);
  156. else
  157. if(length(QRSCount) == 1)
  158. QRSCount_mode_idx = 1;
  159. else
  160. QRSCount_mode_idx = QRSCount_sort_idx(find(diff(QRSCount_sort > median(QRSCount)))); %#ok<FNDSB>
  161. end
  162. end
  164. QRS_channel = channelEEG(QRSCount_mode_idx);
  166. EEG = pop_fmrib_qrsdetect(EEG,QRS_channel(1),'QRS','no'); % FMRIB Toolbox QRS Detection
  167. % disp(find(channelError==1));
  168. end
  170. % Save intermediate
  171. if(saveIntermediates == 1 && isfield(options, 'step2a_qrs'))
  172. if(options.step2a_qrs == 1)
  173. pop_saveset(EEG,'filename',[output_fileName,'_preprocess-2aqrs_eeg'],'filepath',outputDir);
  174. end
  175. end
  177. %% STEP 2b: Pulse Artifact Removal
  178. PAType = 'median'; % Template for pulse artifact (Default: median)
  180. EEG = pop_fmrib_pas(EEG,'QRS',PAType); % Pulse Artifact removal
  182. % Save intermediate
  183. if(saveIntermediates == 1 && isfield(options, 'step2b_pulse'))
  184. if(options.step2b_pulse == 1)
  185. pop_saveset(EEG,'filename',[output_fileName,'_preprocess-2bpulse_eeg'],'filepath',outputDir);
  186. end
  187. end
  189. %% STEP 3: Downsample EEG data to 250Hz
  190. resample_freq = 250;
  191. EEG = pop_resample(EEG,resample_freq);
  193. % Save intermediate
  194. if(saveIntermediates == 1 && isfield(options, 'step3_downsample'))
  195. if(options.step3_downsample == 1)
  196. pop_saveset(EEG,'filename',[output_fileName,'_preprocess-3downsample_eeg'],'filepath',outputDir);
  197. end
  198. end
  200. %% STEP 4: Remove non-EEG channels (i.e., EOG and ECG)
  201. EEG = pop_select(EEG,'nochannel',electrodeExclude);
  203. % Save intermediate
  204. if(saveIntermediates == 1 && isfield(options, 'step4_nonEEG'))
  205. if(options.step4_nonEEG == 1)
  206. pop_saveset(EEG,'filename',[output_fileName,'_preprocess-4nonEEG_eeg'],'filepath',outputDir);
  207. end
  208. end
  210. %% STEP 5: Bandpass filter data
  211. freq_lo = 0.3;
  212. freq_hi = 50;
  213. EEG = pop_eegfiltnew(EEG,'locutoff',freq_lo,'hicutoff',freq_hi);
  215. % Save intermediate
  216. if(saveIntermediates == 1 && isfield(options, 'step5_bandpass'))
  217. if(options.step5_bandpass == 1)
  218. pop_saveset(EEG,'filename',[output_fileName,'_preprocess-5bandpass_eeg'],'filepath',outputDir);
  219. end
  220. end
  222. %% STEP 6: Remove bad channels
  223. EEG = pop_clean_rawdata(EEG,'FlatlineCriterion',5,...
  224. 'ChannelCriterion',0.8,...
  225. 'LineNoiseCriterion',4,...
  226. 'Highpass',[0.75 1.25],...
  227. 'BurstCriterion','off',...
  228. 'WindowCriterion','off',...
  229. 'BurstRejection','off',...
  230. 'Distance','Euclidian',...
  231. 'WindowCriterionTolerances','off');
  233. % Save intermediate
  234. if(saveIntermediates == 1 && isfield(options, 'step6_bad'))
  235. if(options.step6_bad == 1)
  236. pop_saveset(EEG,'filename',[output_fileName,'_preprocess-6bad_eeg'],'filepath',outputDir);
  237. end
  238. end
  240. %% STEP 7: Clear data using ASR
  241. EEG = pop_clean_rawdata(EEG,'FlatlineCriterion','off',...
  242. 'ChannelCriterion','off',...
  243. 'LineNoiseCriterion','off', ...
  244. 'Highpass','off', ...
  245. 'BurstCriterion',20,...
  246. 'WindowCriterion',0.25, ...
  247. 'BurstRejection','on', ...
  248. 'Distance','Euclidian',...
  249. 'WindowCriterionTolerances',[-inf 7]);
  251. % Save intermediate
  252. if(saveIntermediates == 1 && isfield(options, 'step7_asr'))
  253. if(options.step7_asr == 1)
  254. pop_saveset(EEG,'filename',[output_fileName,'_preprocess-7asr_eeg'],'filepath',outputDir);
  255. end
  256. end
  258. %% STEP 8: Rereference data using average reference
  259. EEG = pop_reref(EEG,[]);
  261. % Save intermediate
  262. if(saveIntermediates == 1 && isfield(options, 'step8_reference'))
  263. if(options.step8_reference == 1)
  264. pop_saveset(EEG,'filename',[output_fileName,'_preprocess-8reference_eeg'],'filepath',outputDir);
  265. end
  266. end
  268. %% STEP 9: Compute ICA, flat IC using ICLabel, and remove ICs highly correlated with muscle and eye artifacts
  269. EEG = pop_runica(EEG,'icatype','runica','concatcond','on','options',{'pca',-1});
  270. EEG = pop_iclabel(EEG,'default');
  271. EEG = pop_icflag(EEG,[NaN NaN; 0.8 1; 0.8 1; NaN NaN; NaN NaN; NaN NaN; NaN NaN]);
  272. EEG = pop_subcomp(EEG,[]);
  274. % Save intermediate
  275. if(saveIntermediates == 1 && isfield(options, 'step9_ica'))
  276. if(options.step9_ica == 1)
  277. pop_saveset(EEG,'filename',[output_fileName,'_preprocess-9ica_eeg'],'filepath',outputDir);
  278. end
  279. end
  281. %% STEP 10: Save preprocessing data into output directory
  282. if(isempty(saveIntermediates) || options.final == 1)
  283. pop_saveset(EEG,'filename',[output_fileName,'_preprocess_eeg'],'filepath',outputDir);
  284. end
  286. end
  287. %% Subfunction: Extract Event Latency
  288. function [eventLatency,event_idx] = extract_eventLatency(EEG,eventType)
  290. X = zeros(length(EEG.event),1);
  291. eventLatency = zeros(length(EEG.event),1);
  293. for ii = 1:length(EEG.event)
  294. if(strcmp(EEG.event(ii).type,eventType))
  295. X(ii) = 1;
  296. eventLatency(ii) = EEG.event(ii).latency;
  298. end
  299. end
  301. eventLatency(X==0) = [];
  302. event_idx = find(X==1);
  303. end

natview_eeg_preprocess_pipeline.m at commit a7ac63d, under CC-BY-SA-4.0 · at the source

Overview

Authors: Younghwa Cha1,2,3, Yeji Lee1,2, Eunhee Ji1,2, SoHyun Han4, Sunhyun Min1,2,5, Hyoungkyu Kim1,2,3, Minseo Cho6, Hae Seong Lee7, Youngjai Park1,2, Joon-Young Moon1,2
ORCID iDs: Younghwa Cha, Hae Seong Lee, Joon-Young Moon
  1. Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, 16419 Republic of Korea
  2. Sungkyunkwan University, Suwon, 16419 Republic of Korea
  3. Research institute of Slowave Inc., Seoul, 06160 Republic of Korea
  4. Center for Bio-imaging and Translational Research, Korea Basic Science Institute, Ochang, 28119 Republic of Korea
  5. Department of Metabiohealth, Sungkyunkwan University, Suwon, 16419 Republic of Korea
  6. Department of Psychology and Neuroscience, Boston College, Chestnut Hill, Massachusetts 02467 USA
  7. Department of Physics, Sungkyunkwan University, Suwon, 16419 Republic of Korea
Institutions: Institute for Basic Science (South Korea); Sungkyunkwan University (South Korea); Korea Basic Science Institute (South Korea); Boston College (United States)
Journal: Scientific data, volume 13, issue 1, article 573
Dates: received 28 March 2025; accepted 13 January 2026; published online 3 March 2026
Type: Data paper · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41597-026-06616-6 · PMID 41771931 · PMCID PMC13066628 · OpenAlex W7133300691
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), EEG (modality), fMRI (modality), human (organism), methods / tools (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Graphs, fMRI & imaging, Evoked potentials, Physiology & signal measures
Keywords: Attention, Sensory processing
MeSH: Attention*, Brain*, Electroencephalography*, Magnetic Resonance Imaging*, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Humans (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Research Foundation of Korea (NRF) (2019R1A2C2089463, RS-2023-00272652)
Citations: cited by 2 papers (Europe PMC); 38 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.

Repositories

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

nathanklineinstitute/natview_eegfmri

License: CC-BY-SA-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: a7ac63d526e13817e1031e8a910cc58b46a18ae7, 18 July 2025
Languages: Shell (10), MATLAB (7), Python (1)
Size: 44 files, 18 scripts
Software Heritage: not archived
Found in: the text, “Visual task: Checkerboard stimulus”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FSL (10 files), AFNI (8 files), FreeSurfer (7 files), EEGLAB (4 files), ANTs (2 files), ICLabel (2 files), Psychtoolbox (2 files), Image Processing Toolbox (1 file), Signal Processing Toolbox (1 file), NumPy (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
20 files
At the source: github.com/NathanKlineInstitute/NATVIEW_EEGFMRI/blob/main/stimulus/natview_stimuli_checkerboard.m

MoonBrainLab/GradCPT-Simultaneous-EEG-fMRI-DTI-Data

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 1f751e0d1a0154bf64a5951156a49a78453028d6, 16 October 2025
Languages: MATLAB (6), Shell (3)
Size: 11 files, 9 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (3 files), FSL (2 files), EEGLAB (1 file), FreeSurfer (1 file), Signal Processing Toolbox (1 file), MRtrix3 (1 file), Psychtoolbox (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
10 files
At the source: github.com/MoonBrainLab/GradCPT-Simultaneous-EEG-fMRI-DTI-Data

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41597-026-06616-6.

Tracing map

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 27 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);
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Data

Datasets cited

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41597-026-06616-6.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 2 keywords, 7 MeSH terms, 1 funder, 35 references.

Cite

This paper

Cha, Y., Lee, Y., Ji, E., Han, S., Min, S., Kim, H., Cho, M., Lee, H. S., Park, Y., & Moon, J.-Y. (2026). Sustained Attention Task (gradCPT) Dataset using simultaneous EEG-fMRI and DTI. Scientific data, 13(1), 573. https://doi.org/10.1038/s41597-026-06616-6

BibTeX

@article{cha2026sustained,
author = {Cha, Younghwa and Lee, Yeji and Ji, Eunhee and Han, SoHyun and Min, Sunhyun and Kim, Hyoungkyu and Cho, Minseo and Lee, Hae Seong and Park, Youngjai and Moon, Joon-Young},
title = {{Sustained Attention Task (gradCPT) Dataset using simultaneous EEG-fMRI and DTI}},
journal = {Scientific data},
year = {2026},
month = mar,
volume = {13},
number = {1},
pages = {573},
publisher = {Nature Publishing Group},
issn = {2052-4463},
doi = {10.1038/s41597-026-06616-6},
url = {https://doi.org/10.1038/s41597-026-06616-6},
pmid = {41771931},
pmcid = {PMC13066628}
}

RIS

TY - JOUR
AU - Cha, Younghwa
AU - Lee, Yeji
AU - Ji, Eunhee
AU - Han, SoHyun
AU - Min, Sunhyun
AU - Kim, Hyoungkyu
AU - Cho, Minseo
AU - Lee, Hae Seong
AU - Park, Youngjai
AU - Moon, Joon-Young
TI - Sustained Attention Task (gradCPT) Dataset using simultaneous EEG-fMRI and DTI
T2 - Scientific data
J2 - Sci Data
PY - 2026
DA - 2026/03/03
VL - 13
IS - 1
SP - 573
SN - 2052-4463
PB - Nature Publishing Group
DO - 10.1038/s41597-026-06616-6
UR - https://doi.org/10.1038/s41597-026-06616-6
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41597-026-06616-6",
"type": "article-journal",
"title": "Sustained Attention Task (gradCPT) Dataset using simultaneous EEG-fMRI and DTI",
"container-title": "Scientific data",
"author": [
{
"family": "Cha",
"given": "Younghwa"
},
{
"family": "Lee",
"given": "Yeji"
},
{
"family": "Ji",
"given": "Eunhee"
},
{
"family": "Han",
"given": "SoHyun"
},
{
"family": "Min",
"given": "Sunhyun"
},
{
"family": "Kim",
"given": "Hyoungkyu"
},
{
"family": "Cho",
"given": "Minseo"
},
{
"family": "Lee",
"given": "Hae Seong"
},
{
"family": "Park",
"given": "Youngjai"
},
{
"family": "Moon",
"given": "Joon-Young"
}
],
"container-title-short": "Sci Data",
"volume": "13",
"issue": "1",
"page": "573",
"DOI": "10.1038/s41597-026-06616-6",
"PMID": "41771931",
"PMCID": "PMC13066628",
"ISSN": "2052-4463",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41597-026-06616-6",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
3
]
]
}
}

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