Bursts of regional cortical inhibition during smartphone use.

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1. [1] § STAR★Methods › Method details › EEG recording and preprocessing ↔ ANLgetbetaBurst.m, the whole file · a weak match · score 0.86 · clean_channels, pop_interp, spherical, artifact, EEGLAB, inactive
2. [2] § STAR★Methods › Method details › EEG recording and preprocessing ↔ src/EEG_analysis/preprocess_EEG/gettechnincallycleanEEG.m, the whole file · a weak match · score 0.79 · pop_interp, bad channels, muscle, spherical, artifact, EEGLAB
3. [3] § STAR★Methods › Quantification and statistical analysis › β-burst occupancy (BO) ↔ ANLgetBO.m, the whole file · a weak match · score 0.61 · burst occupancy, recording duration, burst duration, BO

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The authors' code

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MATLAB · 142 lines · 5.6 KB · no license · 1 match

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• The repository at the source (CODELABCODELIB/BetaburstInhibition_2025)
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1. function ANLgetbetaBurst(inpath, outpath,subID)
2. % Wenyu wan, Leiden univerisity, 09/2024, edited: 15/05/2025
3. cd ([inpath subID]);
4. setlist = dir('1*.set');
5. setname = {setlist.name};
7. % inter-beta burst onset interval JID
8. for s = 1:length(setname)
9. outdir = strcat(outpath,subID);
10. mkdir(outdir)
12. load('/home/wanw1/toolbox/beta_burst/Orignalchanlocs.mat');
13. % load dataset/d
14. cd ([inpath subID]);
15. EEG = pop_loadset(strcat(inpath, subID,'/', setname{1,s}));
17. % remove artifact IC
18. outdir_tmp = [pwd filesep 'amicaout'];
19. cd(outdir_tmp);
20. modout = loadmodout15(outdir_tmp);
21. model_index = 1;
22. EEG.icawinv = modout.A(:,:,model_index);
23. EEG.icaweights = modout.W(:,:,model_index);
24. EEG.icasphere = modout.S;
25. EEG = eeg_checkset(EEG);
26. EEG = pop_iclabel(EEG,'default');
27. EEG = pop_icflag(EEG,[NaN NaN;0.9 1;0.9 1;0.9 1;0.9 1;0.9 1;0.9 1]);
28. EEG = pop_subcomp(EEG,[],0,0);
30. % remove eye channels
31. EEG = pop_select(EEG, 'nochannel',{'E5' 'E64'});
33. % beta-band filtering
34. EEG = pop_eegfiltnew(EEG, [],45);
35. EEG = pop_eegfiltnew(EEG, 1,[]);
37. % remove unrelevant channels
38. %EEG = pop_rejchan(EEG, 'threshold', 3, 'norm', 'on', 'measure', 'spec','freqrange',[13 30]);
39. EEG = clean_channels(EEG,0.85);
41. % interpolate
42. EEG = pop_interp(EEG,Orignalchanlocs,'spherical');
44. % reject inactive data segment
45. EEG = rejectsegment(EEG,0,0);
47. % epoch
48. INEEG = pop_epoch(EEG,{'Phone'},[-3 3],'epochinfo','no');
49. INEEG = selectepoch(INEEG);
51. beta_burst_tap = nan(62,length(INEEG.modepoch),6001);
52. iti = nan(62,length(INEEG.modepoch));
53. burst_rate_iti = nan(62,length(INEEG.modepoch));
55. % Define parameters
56. for c= 1:62 % Channel to analyze
57. fre_band = [1:1:45]; % Beta-band frequency range (Hz)
58. beta_band = [13:1:30];
59. fs = 1000;
60. m = 7;
61. time = -1:1/fs:1;
62. %Create time-frequency power matrix
63. wavelet_conv_data = zeros(length(fre_band),EEG.pnts);
64. % from paper
65. for f_idx = 1:length(fre_band)
66. % freqs and cycles
67. freq = fre_band(f_idx);
68. sigma = m/(2*pi*freq);
69. % sine and gaussian
70. sine_wave = exp(1i*2*pi*freq.*time);
71. gaussian_win = exp(-time.^2./(2*sigma^2));
72. % normalization factor
73. normalization_factor = 1 / (sigma * sqrt(2* pi));
74. % make wavelet
75. wavelet = normalization_factor .* sine_wave .* gaussian_win;
76. halfwaveletsize = ceil(length(wavelet)/2); % half of the wavelet size
77. % convolve with data
78. n_conv = length(wavelet) + EEG.pnts - 1; % compute Gaussian
79. % fft
80. fft_w = fft(wavelet,n_conv);
81. fft_e = fft(EEG.data(c,:),n_conv);
82. ift = ifft(fft_e.*fft_w,n_conv);
83. wavelet_conv_data(f_idx,:) = abs(ift(halfwaveletsize:end-halfwaveletsize+1)).^2;
84. end
86. detected_tf = zeros(size(wavelet_conv_data,1),size(wavelet_conv_data,2));
87. % power threshold
88. burst_cutoff = 6*nanmedian(wavelet_conv_data');
89. % detect beta-burst based on power threshold
90. for f_idx = 1:length(beta_band)
91. detected_tf(f_idx,:) = wavelet_conv_data(f_idx,:)>burst_cutoff(f_idx);
92. end
93. % futher check based on duration burst
95. detected_tf_new = zeros(size(detected_tf,1),size(detected_tf,2));
96. for f_idx = 1:length(fre_band)
97. detected_tf(f_idx,:) = wavelet_conv_data(f_idx,:)>burst_cutoff(f_idx);
99. betaBurstInds = SplitVec(find(detected_tf(f_idx,:)),'consecutive');
100. segL = cellfun('length',betaBurstInds); % Find burst lengths
101. burstSelInds = segL>((1000/fre_band(f_idx))*2); % Select bursts with above min length
102. burstSelIndsout = betaBurstInds(burstSelInds);
103. for i = 1:length(burstSelIndsout)
104. detected_tf_new(f_idx,burstSelIndsout{1,i}) = 1;
105. end
106. end
108. detected_idx = (nansum(detected_tf_new(beta_band,:),1)>0);
109. freq_prob(c,:) = nanmean(detected_tf_new(beta_band,:),2);
110. % [M,I] = max(freq_prob(c,:));
111. % freq_M(c) = M;
112. % freq_I(c) = beta_band(I);
114. for e = 2: length(INEEG.modepoch)
115. try
116. beta_burst_tap(c,e,:) = detected_idx(INEEG.urevent(INEEG.modepoch(e).eventurevent).latency-3000:INEEG.urevent(INEEG.modepoch(e).eventurevent).latency+3000);
117. %tf_tap(c,e,:,:) = wavelet_conv_data(beta_band,INEEG.urevent(INEEG.modepoch(e).eventurevent).latency-3000:INEEG.urevent(INEEG.modepoch(e).eventurevent).latency+3000);
118. iti(c,e) = (INEEG.urevent(INEEG.modepoch(e).eventurevent).latency-INEEG.urevent(INEEG.modepoch(e-1).eventurevent).latency);
119. burst_rate_iti(c,e) = nansum(detected_idx(INEEG.urevent(INEEG.modepoch(e-1).eventurevent).latency:INEEG.urevent(INEEG.modepoch(e).eventurevent).latency))/iti(c,e);
120. tap_idx(e) = INEEG.urevent(INEEG.modepoch(e).eventurevent).latency;
121. %iti_1(e) = (INEEG.urevent(INEEG.modepoch(e+1).eventurevent).latency-INEEG.urevent(INEEG.modepoch(e).eventurevent).latency);
122. end
123. end
126. % burst rate: burst duration/whole duration
127. % burst_rate_whole(c) = sum(detected_idx>0)/length(detected_idx);
128. beta_burst_dur(c) = nansum(detected_idx>0);
129. beta_burst_dur_allfre(c,:) = nansum(detected_tf_new(beta_band,:)>0,2);
130. recording_dur(c) = length(detected_idx);
132. % calculate tap num during beta burst
133. betaburst_idx = find(detected_idx>0);
134. tapnum(c) = nansum(ismember(tap_idx,betaburst_idx));
136. end
137. cd (outdir);
138. filename = erase(setname{1,s},'.set');
139. save(strcat(filename,'.mat'),'burst_rate_iti','iti','beta_burst_dur','beta_burst_dur_allfre','recording_dur','beta_burst_tap','freq_prob','tapnum','-v7.3');
140. end
142. end

ANLgetbetaBurst.m at commit 23ffe9f, no license · at the source

Overview

Authors: Wenyu Wan^1,2, Arko Ghosh¹

1. Cognitive Psychology Unit, Institute of Psychology, Leiden University, Wassenaarseweg 52, Leiden 2333 AK, the Netherlands
2. Department of Psychology, University of Amsterdam, Nieuwe Achtergracht 129, Amsterdam 1001 NK, the Netherlands

Institutions: Leiden University (Netherlands); University of Amsterdam (Netherlands)

Journal: iScience, volume 29, issue 4, article 115375

Dates: received 30 September 2025; accepted 12 March 2026; published online 16 March 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1016/j.isci.2026.115375 · PMID 41972003 · PMCID PMC13068570 · OpenAlex W7137171762

Open access: gold, a free copy (OpenAlex)

Status: code verified

Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Physiology & signal measures

Keywords: Neuroscience, Behavioral neuroscience, Cognitive neuroscience, Biomedical engineering, Psychology

Topic: Impact of Technology on Adolescents (Sociology and Political Science, Social Sciences), according to OpenAlex

Funding: Velux Stiftung (1283); China Scholarship Council (202004910349)

Citations: not cited yet (Europe PMC); 50 references in the paper

Research resources: MATLAB RRID:SCR_001622, EEGlab 2024 RRID:SCR_007292, LIMO EEG toolbox RRID:SCR_009592

Abstract

Transient beta (β) bursts—brief neural events—are increasingly recognized for their role in motor and cognitive processes, yet their contribution to naturalistic behavior remains poorly understood. Smartphone behavior, involving continuous varied engagement, provides a rich real-world context to examine these dynamics. We recorded electroencephalography (EEG) as participants interacted with their smartphones using their right thumb for ∼80 min. β-bursts were detected across the scalp, with bilateral sensorimotor electrodes accumulating more bursts than other electrodes. Brain-wide burst probability decreased before touchscreen touches and increased afterward, with the strongest modulation over left sensorimotor cortex. Touches occasionally occurred during bursts, but at lower rates over left sensorimotor regions than elsewhere. Separating touchscreen intervals with versus without bursts revealed an exaggerated difference in sensorimotor cortex: intervals containing bursts were longer. These patterns suggest that continuous smartphone touches are modulated by brief, spatially localized transient bursts that may gate motor execution in sensorimotor networks.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

CODELABCODELIB/JID_ERP_Smartphone_2024

License: none: the authors keep all their rights

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: 10a90cbbb3f71ba23746cfeefd35910f296a3398, 13 May 2025

Languages: MATLAB (22)

Size: 30 files, 22 scripts

Software Heritage: not archived

Found in: “Data and code availability”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: EEGLAB (7 files), Statistics and Machine Learning Toolbox (7 files), ICLabel (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

23 files

• ERP/gatherGFP.m, MATLAB, 52 lines
• ERP/main_ANLgetERP.m, MATLAB, 113 lines
• ICclustering/ANL_icclustering.m, MATLAB, 95 lines
• ICclustering/ANLprepareIC.m, MATLAB, 37 lines
• ICerp/add_epoch.m, MATLAB, 50 lines
• ICerp/getICerp.m, MATLAB, 160 lines
• Moran/find_adj_matrix.m, MATLAB, 24 lines
• Moran/main_moran.m, MATLAB, 43 lines
• Moran/smoothedJID2.m, MATLAB, 125 lines
• NNMF/main_ANLnnmf.m, MATLAB, 50 lines
• NNMF/nnmf_plot.m, MATLAB, 124 lines
• NNMF/run_icaerp_nnmf2.m, MATLAB, 80 lines
• add_epoch.m, MATLAB, 50 lines
• alignment/ANLalig_acrossdataset.m, MATLAB, 77 lines
• alignment/ANLgetBSmodeldata.m, MATLAB, 183 lines
• alignment/main_ANLalignment.m, MATLAB, 46 lines
• alignment/main_getBSRrange.m, MATLAB, 63 lines
• alignment/main_getBSrange.m, MATLAB, 69 lines
• alignment/main_rejectsubBS.m, MATLAB, 51 lines
• alignment/main_rejectsubBS_R.m, MATLAB, 40 lines
• preprocessing/ANLpreprocessing.m, MATLAB, 92 lines
• preprocessing/main_runica.m, MATLAB, 41 lines
• readme.md, Text, 37 lines

At the source: github.com/CODELABCODELIB/JID_ERP_Smartphone_2024

CODELABCODELIB/BetaburstInhibition_2025

License: none: the authors keep all their rights

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: 23ffe9ff2b75e1831bc4d6d69151f83576e13016, 22 August 2025

Languages: MATLAB (5)

Size: 5 files, 5 scripts

Software Heritage: not archived

Found in: “Data and code availability”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: EEGLAB (5 files), Statistics and Machine Learning Toolbox (3 files), ICLabel (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers

• 30 September 2026: the link answers

5 files

• ANLgetBBTI.m, MATLAB, 57 lines
• ANLgetBO.m, MATLAB, 36 lines, 1 match
• ANLgetBPI.m, MATLAB, 65 lines
• ANLgetBTR.m, MATLAB, 53 lines
• ANLgetbetaBurst.m, MATLAB, 142 lines, 1 match

At the source: github.com/CODELABCODELIB/BetaburstInhibition_2025

CODELABLEIDEN/Non_goal_directed_smartphone_2022

License: MIT

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: e275fc864c5eb081fac316e870854dd57d9587ce, 9 March 2022

Languages: MATLAB (48), JavaScript (10), Python (6), Shell (1), Jupyter (1)

Size: 165 files, 66 scripts

Software Heritage: not archived

Found in: the resources table

Holds: README, license file, environment (requirements.txt), documentation

Not found: CITATION.cff, tests, continuous integration

Tools: EEGLAB (9 files), scikit-learn (4 files), h5py (3 files), Keras (3 files), Signal Processing Toolbox (3 files), Matplotlib (3 files), NumPy (3 files), pandas (3 files), TensorFlow (2 files), Statistics and Machine Learning Toolbox (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers

• 30 September 2026: the link answers

68 files

• docs/_build/html/_static/doctools.js, JavaScript, 321 lines
• docs/_build/html/_static/documentation_options.js, JavaScript, 12 lines
• docs/_build/html/_static/jquery-3.5.1.js, JavaScript, 7,429 lines
• docs/_build/html/_static/jquery.js, JavaScript, 2 lines
• docs/_build/html/_static/language_data.js, JavaScript, 297 lines
• docs/_build/html/_static/searchtools.js, JavaScript, 522 lines
• docs/_build/html/_static/sidebar.js, JavaScript, 159 lines
• docs/_build/html/_static/underscore-1.12.0.js, JavaScript, 2,027 lines
• docs/_build/html/_static/underscore.js, JavaScript, 6 lines
• docs/_build/html/searchindex.js, JavaScript, 1 line
• docs/conf.py, Python, 245 lines
• get_started.sh, Shell, 10 lines
• src/EEG_analysis/ERP_ERSP_data_generation/generate_erp_data.m, MATLAB, 76 lines
• src/EEG_analysis/ERP_ERSP_data_generation/generate_spectral_data.m, MATLAB, 111 lines
• src/EEG_analysis/GLM/create_design_matrix_model.m, MATLAB, 12 lines
• src/EEG_analysis/GLM/generate_ancova_spectral_data.m, MATLAB, 113 lines
• src/EEG_analysis/GLM/linear_model.m, MATLAB, 47 lines
• src/EEG_analysis/GLM/main_GLM.m, MATLAB, 23 lines
• src/EEG_analysis/GLM/run_clustering_linear.m, MATLAB, 9 lines
• src/EEG_analysis/GLM/t_test_linear_mod.m, MATLAB, 47 lines
• src/EEG_analysis/preprocess_EEG/gettechnincallycleanEEG.m, MATLAB, 65 lines, 1 match
• src/EEG_analysis/t_tests_ERP_and_ERSP/rename_add_suffix.m, MATLAB, 26 lines
• src/EEG_analysis/t_tests_ERP_and_ERSP/run_clustering.m, MATLAB, 73 lines
• src/EEG_analysis/t_tests_ERP_and_ERSP/run_t_tests_and_clustering.m, MATLAB, 100 lines
• src/__init__.py, Python, 1 line
• src/alignment/decision_tree/decision_peak_prominance.m, MATLAB, 31 lines
• src/alignment/decision_tree/decision_tree_alignment.m, MATLAB, 104 lines
• src/alignment/features/create_hdf_MA.m, MATLAB, 45 lines
• src/alignment/features/create_matrix_MA.m, MATLAB, 39 lines
• src/alignment/features/preprocess.m, MATLAB, 67 lines
• src/alignment/features/seperate_FS_sets.m, MATLAB, 56 lines
• src/alignment/predict/main_lstm_MA.m, MATLAB, 28 lines
• src/alignment/predict/predict_MA.m, MATLAB, 45 lines
• src/alignment/training/lstm_MA.py, Python, 243 lines
• src/ams_nams/add_events.m, MATLAB, 44 lines
• src/ams_nams/calculate_date_differences.m, MATLAB, 22 lines
• src/ams_nams/find_AM_NAM_AT.m, MATLAB, 33 lines
• src/ams_nams/find_ams_n_nams.m, MATLAB, 54 lines
• src/ams_nams/find_nearest.m, MATLAB, 34 lines
• src/ams_nams/find_nearest_distances.m, MATLAB, 55 lines
• src/ams_nams/get_ams_nam.m, MATLAB, 28 lines
• src/ams_nams/pred_events.m, MATLAB, 85 lines
• src/ams_nams/set_parameters.m, MATLAB, 110 lines
• src/bs_to_tap/features/change_sequence_precision.m, MATLAB, 26 lines
• src/bs_to_tap/features/create_hdf5.m, MATLAB, 39 lines
• src/bs_to_tap/features/delta_mod.m, MATLAB, 21 lines
• src/bs_to_tap/features/delta_sigma_mod.m, MATLAB, 17 lines
• src/bs_to_tap/features/prepare_features.m, MATLAB, 79 lines
• src/bs_to_tap/features/seperate_sequences.m, MATLAB, 28 lines
• src/bs_to_tap/predict/.ipynb_checkpoints/Untitled-checkpoint.ipynb, Jupyter, 1 line
• src/bs_to_tap/predict/.ipynb_checkpoints/predict_test-checkpoint.py, Python, 609 lines
• src/bs_to_tap/predict/predictions_bs_to_tap.py, Python, 162 lines
• src/bs_to_tap/predict/read_predictions.m, MATLAB, 78 lines
• src/bs_to_tap/predict/save_predictions.m, MATLAB, 180 lines
• src/bs_to_tap/training/bs_to_tap.py, Python, 498 lines
• src/utils/call_func_for_all_participants.m, MATLAB, 80 lines
• src/utils/call_func_for_all_participants_eeg.m, MATLAB, 170 lines
• src/utils/getBSdata.m, MATLAB, 101 lines
• src/utils/get_phone_data.m, MATLAB, 46 lines
• src/utils/getcleanedbsdata.m, MATLAB, 47 lines
• src/utils/getepocheddata.m, MATLAB, 29 lines
• src/utils/importfile_dateBS.m, MATLAB, 56 lines
• src/utils/mergeStructs.m, MATLAB, 33 lines
• src/utils/merge_file_BS_epoched.m, MATLAB, 25 lines
• src/utils/nanzscore.m, MATLAB, 8 lines
• src/utils/remove_inactive_bs.m, MATLAB, 36 lines
• LICENSE, License, 10 lines
• README.md, Text, 42 lines

At the source: github.com/CODELABLEIDEN/Non_goal_directed_smartphone_2022

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

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:

• 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
• 93 scripts, each with its path and the digest of its content;
• 3 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

• osf:k27n5, at OSF; found in “Data and code availability”

Data and code availability

The processed data have been deposited at https://osf.io/k27n5/overview.

Code for preprocessing analysis has been deposited at https://github.com/CODELABCODELIB/JID_ERP_Smartphone_2024; code for β-burst identification and subsequent statistical analyses are shared at https://github.com/CODELABCODELIB/BetaburstInhibition_2025.

Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 5 keywords, 2 funders, 48 references, 3 RRIDs.

Cite

This paper

Wan, W., & Ghosh, A. (2026). Bursts of regional cortical inhibition during smartphone use. iScience, 29(4), 115375. https://doi.org/10.1016/j.isci.2026.115375

BibTeX

@article{wan2026bursts,
author = {Wan, Wenyu and Ghosh, Arko},
title = {{Bursts of regional cortical inhibition during smartphone use}},
journal = {iScience},
year = {2026},
month = mar,
volume = {29},
number = {4},
pages = {115375},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.115375},
url = {https://doi.org/10.1016/j.isci.2026.115375},
pmid = {41972003},
pmcid = {PMC13068570}
}

RIS

TY - JOUR
AU - Wan, Wenyu
AU - Ghosh, Arko
TI - Bursts of regional cortical inhibition during smartphone use
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/03/16
VL - 29
IS - 4
SP - 115375
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.115375
UR - https://doi.org/10.1016/j.isci.2026.115375
LA - en
ER -

CSL-JSON

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• CODELABLEIDEN/Non_goal_directed_smartphone_2022

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