OSCR

The Brain, Body, and Behavior Dataset (BBBD): Multimodal Recordings during Educational Videos.

Code ↔ Paper

19 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 19 matches
  1. [1] § Methods › Signal acquisition › Recording and preprocessing of EEG ↔ data-reformatting/bids/raw/bbbd_123_raw_eeg.m, lines 192–238 · score 0.89 · notch filtered, low pass filtering, high pass filtered, ActiveTwo, EEG channels, raw EEG
  2. [2] § Methods › Signal acquisition › Recording and preprocessing of EEG ↔ data-reformatting/bids/raw/bbbd_45_raw_eeg.m, lines 227–274 · score 0.88 · notch filtered, low pass filtering, high pass filtered, ActiveTwo, EEG channels, raw EEG
  3. [3] § Background & Summary ↔ data-preprocessing/postprocessEyelinkData.m, lines 1–60 · score 0.78 · fixation rate, head position, gaze position, blink rate, saccade rate, horizontal
  4. [4] § Methods › Signal acquisition › Recording and preprocessing of ECG ↔ data-reformatting/bids/raw/bbbd_123_raw_eeg.m, lines 192–238 · score 0.73 · notch filtered, high pass filtered, ActiveTwo, cutoff, BioSemi, MATLAB
  5. [5] § Methods › Signal acquisition › Recording and preprocessing of ECG ↔ data-reformatting/bids/raw/bbbd_45_raw_eeg.m, lines 227–274 · score 0.71 · notch filtered, high pass filtered, ActiveTwo, cutoff, BioSemi, ECG
  6. [6] § Methods › Signal acquisition › Recording and preprocessing of gaze position, head position, and pupil size ↔ data-preprocessing/preprocessEyelinkData.m, lines 1–54 · score 0.69 · EyeLink, head movements, Gaze position, preprocessing, pupil
  7. [7] § Methods › Signal acquisition › Recording and preprocessing of gaze position, head position, and pupil size ↔ data-reformatting/bids/raw/bbbd_123_raw_eye_heart.m, lines 141–189 · score 0.62 · camera sensor pixels, millimeters, distance, Head, signals, eye
  8. [8] § Methods › Signal acquisition › Recording and preprocessing of gaze position, head position, and pupil size ↔ data-reformatting/bids/raw/bbbd_45_raw_eye_heart.m, lines 177–225 · score 0.62 · camera sensor pixels, millimeters, distance, Head, signals, eye
  9. [9] § Methods › Signal acquisition › Recording and preprocessing of gaze position, head position, and pupil size ↔ data-preprocessing/postprocessEyelinkData.m, lines 1–60 · score 0.61 · head movements, Gaze position, EyeLink, pupil, preprocessing
  10. [10] § Methods › Signal acquisition › Recording and preprocessing of respiration ↔ data-reformatting/bids/raw/bbbd_123_raw_eye_heart.m, lines 214–260 · score 0.60 · BioSemi, belt worn, Respiration signal, tension, chest
  11. [11] § Methods › Signal acquisition › Recording and preprocessing of respiration ↔ data-reformatting/bids/raw/bbbd_45_raw_eye_heart.m, lines 250–296 · score 0.60 · BioSemi, belt worn, Respiration signal, tension, chest
  12. [12] § Technical Validation › Gaze, saccades, blinks and fixations ↔ data-preprocessing/preprocessEyelinkData.m, lines 1–54 · score 0.59 · eye movement, gaze position, detection, saccade, blink
  13. [13] § Technical Validation › EEG ↔ figures-reproduction/figures-paper.ipynb, lines 32–158 · score 0.55 · power spectral density, occipital, frontal, EEG
  14. [14] § Methods › Procedure ↔ data-reformatting/bbbd_generate_bids_descriptors.m, lines 110–124 · score 0.54 · Institutional Review Boards, City University, approval, York
  15. [15] § Background & Summary ↔ data-reformatting/bids/derived/bbbd_45_derived_discrete.m, lines 1–132 · score 0.52 · breath peak, demographics, timestamps, fixations, saccades, blinks
  16. [16] § Technical Validation › EEG ↔ figures-reproduction/figures-paper.ipynb, lines 160–206 · score 0.52 · Power Spectral Density, SEM, log, band, alpha, EEG
  17. [17] § Methods › Signal acquisition › Recording and preprocessing of gaze position, head position, and pupil size ↔ data-preprocessing/preprocessEyelinkData.m, lines 105–146 · score 0.52 · finding outliers, IQR, median, artifacts, filter, Blinks
  18. [18] § Data Records › Data organization ↔ data-reformatting/bbbd_generate_bids_descriptors.m, lines 60–106 · score 0.50 · digit span scores, ASRS, phenotype, tsv, BIDS, reformatted
  19. [19] § Methods › Signal acquisition › Recording and preprocessing of EEG ↔ data-preprocessing/preprocessEEGdata.m, lines 199–276 · score 0.50 · neighboring electrodes, interquartile, Outliers, median, preprocessing

Paper

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

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The authors' code

MATLAB · 258 lines · 11 KB · no license · 3 matches

  1. function [data,metadata,idx_missing_data] = preprocessEyelinkData(data,timestamps,metadata,options)
  2. % [data,metadata,data_nan] = preprocessEyelinkData(data,timestamps,metadata,options)
  3. %
  4. % data (samples x modality) : eye link data matrix containing a column per measurement e.g. horizontal, vertical eye movements, pupil and head movements
  5. % timestamps (samples x 1) : column vector with timestamps for each sample
  6. %
  7. % metadata.blinks.timestamps_edf (#blinks x 2): contains a two column
  8. % vector with start and end sample numbers of each blink. This function takes that
  9. % as input and potentially adds more rows if it finds more blinks.
  10. %
  11. % options: this has grown conciderably, check calling function
  12. % mainPupilClean to see an overview.
  13. %
  14. % options.doVisualization = 1 % if you want to see something
  15. %
  16. % jmad/jenma 2022
  17. % jmad/jenma 2025 (updated)
  19. if ~isfield(options,'fs_eyeheadtracking')
  20. options.fs_eyeheadtracking = 1/(mean(diff(timestamps))/1000);
  21. fprintf('No sampking frequency specifief for data etting to %dHz\n',options.fs_eyeheadtracking)
  22. end
  24. fs = options.fs_eyeheadtracking;
  26. % eye tracking extension of blinks (we usually see the onset and offset of blinks detected by the eyelink 1000 to result unreliable detection of
  27. % gaze position and pupil size. We could maybe use saccade data to do this more reliably in the future
  28. buffer_eye_ms = options.buffer_eye_ms; % time on each side of a blink to nan out for eye tracking data (measured in ms)
  29. buffer_eye_samples = round((buffer_eye_ms/1000)*fs); % buffer in samples
  31. % pupil size extension of blink interpolation period
  32. buffer_pupil_ms = options.buffer_pupil_ms; % time on each side of a blink to nan out for pupil data (measured in ms)
  33. buffer_pupil_samples = round((buffer_pupil_ms/1000)*fs); % buffer in samples
  35. % for each EyeLink EDF files these indices might change. Please update according to your specific recording
  36. if size(data,2)>3
  37. idx_eyes = 1:2;
  38. idx_pupil = 3;
  39. idx_resolution = 4:5;
  40. idx_head = 6:8;
  41. end
  43. % this is to keep track of missing data in each of the modalities measured by the eyelink 1000
  44. idx_missing_data.all = false(size(timestamps));
  46. for iMissing = 1:size(metadata.missing_data.sampleidx_edf,1)
  47. idx_missing_data.all(metadata.missing_data.sampleidx_edf(iMissing,1):metadata.missing_data.sampleidx_edf(iMissing,2)) = true;
  48. end
  50. idx_missing_data.eye = idx_missing_data.all;
  51. idx_missing_data.pupil = idx_missing_data.all;
  53. mask_eye = idx_missing_data.all;
  54. mask_pupil = idx_missing_data.all;
  56. %% find blinks and set them to NaN
  57. if options.doBlinkRemoval
  58. fprintft('Removing blinks from eye tracker')
  59. for ii = 1:size(metadata.blinks.timestamps_edf,1)
  61. % Get start and end times of blink
  62. time_blink_start = metadata.blinks.timestamps_edf(ii,1);
  63. time_blink_end = metadata.blinks.timestamps_edf(ii,2);
  65. % Get index for start and end of blink
  66. [~,idx_blink_start] = min(abs(timestamps(:,1) - time_blink_start));
  67. [~,idx_blink_end] = min(abs(timestamps(:,1) - time_blink_end));
  69. % blink length (keep metadata about blinks)
  70. metadata.blinks.duration_samples(ii,1) = length(idx_blink_start:idx_blink_end);
  71. metadata.blinks.duration_time(ii,1) = length(idx_blink_start:idx_blink_end)/fs;
  73. % extend with buffer_samples on each side of blink (eye movements)
  74. idx_blink_eye_start = max(1,idx_blink_start-buffer_eye_samples);
  75. idx_blink_eye_end = min(length(data),idx_blink_end+buffer_eye_samples);
  77. % extend with buffer_samples on each side of blink (pupil size)
  78. idx_blink_pupil_start = max(1,idx_blink_start-buffer_pupil_samples);
  79. idx_blink_pupil_end = min(length(data),idx_blink_end+buffer_pupil_samples);
  81. % fill eye movemetns with nans
  82. mask_eye(idx_blink_eye_start:idx_blink_eye_end,1) = true;
  84. % fill pupil size with nans
  85. mask_pupil(idx_blink_pupil_start:idx_blink_pupil_end,1) = true;
  87. if rem(ii,50)==0
  88. fprintf('.')
  89. end
  90. end
  91. end
  92. fprintf('done\n')
  94. %% do closing operation on mask to remove small spurious outliers
  95. if options.imclose_pupil>0
  96. SE = strel("rectangle",[round(options.imclose_pupil*fs) 1]);
  97. mask_pupil = imclose(mask_pupil,SE);
  98. end
  100. % what is missing now?
  101. idx_missing_data.eye(:,end+1) = any(mask_eye,2);
  102. idx_missing_data.pupil(:,end+1) = any(mask_pupil,2);
  104. %% detect outliers in pupil signal (negative outliers are likely blinks,
  105. %% possitive outliers are likely imagine artefacts) and extend detected to the left and right
  106. if isfield(options, 'doBlinkArtefactRemoval') && options.doBlinkArtefactRemoval
  108. Nsamples = size(data,1);
  110. % only operate on valid samples
  111. idx_valid = ~isnan(data(:,idx_pupil));
  112. data_pupil_valid = data(idx_valid,idx_pupil);
  114. % find outliers
  115. k = 4; % really only extreem outliers
  116. d = data_pupil_valid - medfilt1(data_pupil_valid,round(fs*options.filter_length_mfd_pupil),'truncate'); % deviation from median
  117. mask_positive = d>k*iqr(d);
  118. mask_negative = d<-k*iqr(d);
  120. mask_blinks = false(Nsamples,1);
  121. mask_artifacts = false(Nsamples,1);
  123. clear data_pupil_valid
  125. % mark outlies as missing (in addition to what is already marked from the edf file)
  126. mask_blinks(idx_valid) = mask_negative;
  127. mask_artifacts(idx_valid) = mask_positive;
  129. % set even and odd index if either is set, so as to catch NaN from the upsampling
  130. if mod(find(mask_blinks,1),2)==0
  131. mask_blinks(1:2:end-1) = mask_blinks(1:2:end-1) | mask_blinks(2:2:end);
  132. mask_artifacts(1:2:end-1) = mask_artifacts(1:2:end-1) | mask_artifacts(2:2:end);
  133. else
  134. mask_blinks(2:2:end) = mask_blinks(1:2:end-1) | mask_blinks(2:2:end);
  135. mask_artifacts(2:2:end) = mask_artifacts(1:2:end-1) | mask_artifacts(2:2:end);
  136. end
  138. % finding start and stop of outlier data
  139. d_blinks = diff([0; mask_blinks(1:end-1); 0]); % add zero at begin/end in case that is also bad data
  140. metadata.blinks.timestamps_edf_additional = timestamps([find(d_blinks>0) find(d_blinks<0)]);
  142. d_artifacts = diff([0; mask_artifacts(1:end-1); 0]); % add zero at begin/end in case that is also bad data
  143. metadata.artifacts.timestamps_edf = timestamps([find(d_artifacts>0) find(d_artifacts<0)]);
  144. metadata.artifacts.duration_time = metadata.artifacts.timestamps_edf(:,2) - metadata.artifacts.timestamps_edf(:,1);
  146. timestamps_artifact_blinks = [metadata.blinks.timestamps_edf_additional; metadata.artifacts.timestamps_edf];
  148. %% remove the artifacts and blinks
  149. fprintft('Removing additional blinks/artifacts from eye tracker')
  150. for ii = 1:size(timestamps_artifact_blinks,1)
  151. % Get start and end times of blink
  152. time_blink_artifact_start = timestamps_artifact_blinks(ii,1);
  153. time_blink_artifact_end = timestamps_artifact_blinks(ii,2);
  155. % Get index for start and end of blink
  156. [~,idx_blink_artifact_start] = min(abs(timestamps(:,1) - time_blink_artifact_start));
  157. [~,idx_blink_artifact_end] = min(abs(timestamps(:,1) - time_blink_artifact_end));
  159. % fill pupil size with nans
  160. mask_pupil(idx_blink_artifact_start:idx_blink_artifact_end,2) = true;
  162. if rem(ii,50)==0
  163. fprintf('.')
  164. end
  165. end
  166. end
  168. %% combine all masks
  169. mask_eye = any(mask_eye,2);
  170. mask_pupil = any(mask_pupil,2);
  172. % what is missing now?
  173. idx_missing_data.eye(:,end+1) = any(mask_eye,2);
  174. idx_missing_data.pupil(:,end+1) = any(mask_pupil,2);
  176. %% replace missing data with NaNs
  177. data(mask_eye,idx_eyes) = NaN;
  178. data(mask_pupil,idx_pupil) = NaN;
  180. %% get timestamps of all the interpolated/missing data
  181. d_interpolated_pupil = diff([0; mask_pupil(1:end-1); 0]); % add zero at begin/end in case that is also bad data
  182. d_interpolated_eye = diff([0; mask_eye(1:end-1); 0]); % add zero at begin/end in case that is also bad data
  184. metadata.interpolated_eye.timestamps_edf = timestamps([find(d_interpolated_eye>0) find(d_interpolated_eye<0)]);
  185. metadata.interpolated_pupil.timestamps_edf = timestamps([find(d_interpolated_pupil>0) find(d_interpolated_pupil<0)]);
  187. %% fill in nans with linearly interpolated data
  188. if options.doBlinkInterpolation
  189. % take all the NaNs and use interpolation to fill in to values
  190. data(:,idx_eyes) = fillmissing(data(:,idx_eyes),options.interpolation_method,'EndValues','nearest');
  191. end
  193. if options.doBlinkInterpolation
  194. % take all the NaNs and use interpolation to fill in to values
  195. data(:,idx_pupil) = fillmissing(data(:,idx_pupil),options.interpolation_method,'EndValues','nearest');
  196. end
  198. % head
  199. data(:,idx_head) = fillmissing(data(:,idx_head),options.interpolation_method,'EndValues','nearest');
  201. if options.doVisualization
  202. datanames = {'eye_x','eye_y','pupil'};
  203. Ts = 1/options.fs_eyeheadtracking;
  204. Nsec = Ts*size(data,1);
  205. timeaxis = 0:Ts:Nsec-Ts;
  207. figure('Name','After blink correction','units','normalized','outerposition',[0 0 1 1])
  208. for iCord = 1:3
  209. subplot(3,1,iCord)
  210. plot(timeaxis,data(:,iCord),'k'), hold on
  211. if size(idx_missing_data.pupil,2)==3
  212. tmp = nan(size(data,1),1); tmp(idx_missing_data.pupil(:,3))=data(idx_missing_data.pupil(:,3),iCord); plot(timeaxis,tmp,'b'), hold on
  213. end
  214. tmp = nan(size(data,1),1); tmp(idx_missing_data.pupil(:,2))=data(idx_missing_data.pupil(:,2),iCord); plot(timeaxis,tmp,'r'), hold on
  215. tmp = nan(size(data,1),1); tmp(idx_missing_data.pupil(:,1))=data(idx_missing_data.pupil(:,1),iCord); plot(timeaxis,tmp,'g'), hold off
  217. xlim([min(timeaxis) max(timeaxis)])
  218. title(datanames{iCord})
  219. set(gca,'Position',get(gca,'Position') + [-0.11 -0.0 0.2 0.0])
  220. end
  221. figure
  222. plot(timeaxis,data(:,3),'k'), hold on
  224. end
  226. %% filter the eye tracking data to remove spurious blinks
  227. no_taps_eye = round(fs*options.filter_length_eye);
  229. if no_taps_eye>0
  230. data(:,idx_eyes) = medfilt1(data(:,idx_eyes)-data(1,idx_eyes),no_taps_eye)+data(1,idx_eyes);
  231. end
  233. %% filter the pupil data to remove spurious blinks
  234. no_taps_pupil = round(fs*options.filter_length_pupil);
  236. if no_taps_pupil>0
  237. data(:,idx_pupil) = medfilt1(data(:,idx_pupil)-data(1,idx_pupil),no_taps_eye)+data(1,idx_pupil);
  238. end
  240. %% filter the head movement data to remove spurious movement
  241. no_taps_head = round(fs*options.filter_length_head);
  243. if no_taps_head>0
  244. data(:,idx_head) = medfilt1(data(:,idx_head)-data(1,idx_head),no_taps_head)+data(1,idx_head);
  245. end
  247. %% remove initial sample offsets in data
  248. if data(1,1)~=mean(round(data(fs*0.1,1)))
  249. data(1:5,:) = NaN;
  250. data = fillmissing(data,'linear','EndValues','extrap');
  251. end
  253. fprintf('done\n')
  255. %% check if nan painting succeeded
  256. if any(isnan(data(:)))
  257. error('NaNs not cleaned')
  258. end

preprocessEyelinkData.m at commit 5109afa, no license · at the source

Overview

Authors: Jens Madsen1, Nikhil Kuppa1, Lucas C. Parra1
ORCID iDs: Jens Madsen, Lucas C. Parra
  1. Department of Biomedical Engineering, City College of New York,85 St. Nicholas Terrace, New York, NY 10031 USA
Institutions: City College of New York (United States)
Journal: Scientific data, volume 13, issue 1, article 920
Dates: received 16 February 2025; accepted 2 April 2026; published online 21 April 2026
Type: Data paper · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41597-026-07215-1 · PMID 42014748 · PMCID PMC13284221 · OpenAlex W4410075646
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.04.29.651259
Status: code verified
Categories: EEG (modality), other (modality), human (organism), methods / tools (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Smoothing, state filtering, decompositions, Statistics, fMRI & imaging, Evoked potentials, Physiology & signal measures
Keywords: Attention, Human behaviour, Neurophysiology, Learning and memory
MeSH: Brain*, Adult, Attention, Blinking, Electrocardiography, Electroencephalography, Electrooculography, Female, Humans, Learning, Memory, Short-Term, Pupil, Saccades, Video Recording (* major topic)
Journal subjects: Data Descriptor
Topic: Diverse Music Education Insights (Music, Arts and Humanities), according to OpenAlex
Funding: NSF (DRL-1660548, DRL-2201835)
Citations: cited by 1 paper (Europe PMC); 47 references in the paper

Abstract

The brain, body, and behavior dataset (BBBD) provides multimodal recordings from 178 participants across five experiments in which short educational videos were viewed. The dataset contains about 110 hours of electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG), respiration, pupil size, gaze position, saccades, blinks, fixations, and head-motion signals, all time-aligned to the video and standardized in the Brain Imaging Data Structure (BIDS) format. Participants watched three to six videos (mean = 28 ± 5 min) under conditions that manipulated attention (attentive versus distracted), learning goals (incidental versus intentional), and motivation (monetary incentive). Demographic data, Adult ADHD Self-Report Scale (ASRS) scores, and digit-span working-memory assessments are included. Technical validation shows expected effects: higher alpha-band power during distraction, a posterior-to-anterior gradient in EEG power, increased blink rate, and reduced saccade rate when attention was diverted. Approximately 97 percent of the continuous signal data are newly released. BBBD enables investigation of how neural, ocular, and physiological dynamics relate to attention and learning during naturalistic video viewing.

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 19 matches between paragraphs and lines of code.

madjens/bbbd-dataset

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 5109afa0fd70c407b869108106f5808c6b0cc33c, 23 September 2026
Languages: MATLAB (24), Python (1), Jupyter (1)
Size: 65 files, 26 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: EEGLAB (5 files), Signal Processing Toolbox (5 files), Statistics and Machine Learning Toolbox (2 files), Image Processing Toolbox (1 file), Matplotlib (1 file), MNE-Python (1 file), NumPy (1 file), pandas (1 file), SciPy (1 file), seaborn (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
27 files
At the source: github.com/madjens/bbbd-dataset

Code availability

We have released the code to reproduce the figures shown in this manuscript, convert the data into BIDS format and preprocess the raw signals in the following github repository: https://github.com/madjens/bbbd-dataset. We also released the code to download the datasets using either Python or MATLAB.

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;
  • 26 scripts, each with its path and the digest of its content;
  • 19 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 Brain, Body, and Behavior Dataset (BBBD) is publicly available through Zenodo30 (10.5281/zenodo.19241964) and through the International Neuroimaging Data-Sharing Initiative (INDI)31 (10.15387/fcp_indi.retro.bbbd). The dataset includes neural, physiological, and behavioral data in BIDS format, together with accompanying metadata and documentation.

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, 3 authors, 4 keywords, 14 MeSH terms, 1 funder, 45 references.

Cite

This paper

Madsen, J., Kuppa, N., & Parra, L. C. (2026). The Brain, Body, and Behavior Dataset (BBBD): Multimodal Recordings during Educational Videos. Scientific data, 13(1), 920. https://doi.org/10.1038/s41597-026-07215-1

BibTeX

@article{madsen2026brain,
author = {Madsen, Jens and Kuppa, Nikhil and Parra, Lucas C.},
title = {{The Brain, Body, and Behavior Dataset (BBBD): Multimodal Recordings during Educational Videos}},
journal = {Scientific data},
year = {2026},
month = apr,
volume = {13},
number = {1},
pages = {920},
publisher = {Nature Publishing Group},
issn = {2052-4463},
doi = {10.1038/s41597-026-07215-1},
url = {https://doi.org/10.1038/s41597-026-07215-1},
pmid = {42014748},
pmcid = {PMC13284221}
}

RIS

TY - JOUR
AU - Madsen, Jens
AU - Kuppa, Nikhil
AU - Parra, Lucas C.
TI - The Brain, Body, and Behavior Dataset (BBBD): Multimodal Recordings during Educational Videos
T2 - Scientific data
J2 - Sci Data
PY - 2026
DA - 2026/04/21
VL - 13
IS - 1
SP - 920
SN - 2052-4463
PB - Nature Publishing Group
DO - 10.1038/s41597-026-07215-1
UR - https://doi.org/10.1038/s41597-026-07215-1
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41597-026-07215-1",
"type": "article-journal",
"title": "The Brain, Body, and Behavior Dataset (BBBD): Multimodal Recordings during Educational Videos",
"container-title": "Scientific data",
"author": [
{
"family": "Madsen",
"given": "Jens"
},
{
"family": "Kuppa",
"given": "Nikhil"
},
{
"family": "Parra",
"given": "Lucas C."
}
],
"container-title-short": "Sci Data",
"volume": "13",
"issue": "1",
"page": "920",
"DOI": "10.1038/s41597-026-07215-1",
"PMID": "42014748",
"PMCID": "PMC13284221",
"ISSN": "2052-4463",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41597-026-07215-1",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
21
]
]
}
}

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