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The impact of Independent Component Analysis on TMS-evoked potentials: a within-subject comparison across motor and prefrontal areas.

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  1. [1] § CRediT Authorship contribution statement ↔ preprocessing_1.m, lines 1–7 · score 0.55 · Timo van Hattem, Angremont, Emile
  2. [2] § CRediT Authorship contribution statement ↔ preprocessing_2.m, lines 1–6 · score 0.55 · Timo van Hattem, Angremont, Emile

Paper

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The authors' code

MATLAB · 125 lines · 5.8 KB · no license · 1 match

  1. % PREPROCESSING TMS-EEG DATA
  2. %
  3. % Timo van Hattem
  4. % Updated: 13-3-2023
  5. % Adjusted by Emile d'Angremont, 13-9-2023
  7. cd('/scratch/anw/edangremont/TMS-EEG/code/')
  8. %% Clean workspace
  9. clear
  10. close all
  11. clc
  13. %% Set path
  14. %addpath('/data/anw/anw-gold/NP/projects/data_TIPICCO/TMS_EEG/tvh/eeglab2023.0/');
  15. %addpath(genpath('/data/anw/anw-gold/NP/projects/data_TIPICCO/TMS_EEG/tvh/eeglab2023.0/FastICA_25/'));
  16. addpath(genpath('/scratch/anw/edangremont/TMS-EEG/data/'));
  17. addpath('/scratch/anw/edangremont/TMS-EEG/code/');
  18. addpath('/scratch/anw/edangremont/TMS-EEG/code/eeglab2023.0');
  19. % addpath(genpath('/scratch/anw/edangremont/TMS-EEG/code/eeglab2023.0/FastICA_25/'));
  20. fprintf('Paths added!\n')
  22. %% Initialize variables
  23. ppn = 'TC923'; %input subject number % make gui out of this
  24. br = 'rDLPFC'; %input brain region of interest
  26. % set input and output paths
  27. DATAIN = ['/scratch/anw/edangremont/TMS-EEG/data/raw/', ppn, '/', br, '/'];
  28. %DATAIN = ['/scratch/anw/tvanhattem/analysis_tvh/TMSEEG_data/convert/', ppn, '/', br, '/'];
  29. DATAOUT = '/scratch/anw/edangremont/TMS-EEG/data/processed/';
  31. %% Import data
  32. eeglab;
  33. EEG = loadcurry([DATAIN, dir(fullfile(DATAIN, '*.cdt')).name], 'KeepTriggerChannel', 'False', 'CurryLocations', 'False');
  34. %EEG = pop_biosig(); %for loading convert file
  35. fprintf('N_events start processing: %d\n', length(EEG.event)); % manually add to excel file?
  37. %% Load channel locations
  38. EEG = pop_chanedit(EEG,'lookup','/scratch/anw/edangremont/TMS-EEG/code/eeglab2023.0/plugins/dipfit/standard_BEM/elec/standard_1020.elc');
  40. %% Remove unused electrodes
  41. EEG = pop_select(EEG, 'nochannel', 63:68); % these are not used
  42. EEG.allchan = EEG.chanlocs;
  44. %% Automated removal bad electrodes step 1
  45. EEG = pop_clean_rawdata(EEG, 'FlatlineCriterion',5,'Highpass', 'off','ChannelCriterion',0.8,...
  46. 'LineNoiseCriterion',4,'BurstCriterion','off','WindowCriterion','off'); % should these parameters be saved somewhere?
  47. % EEG.rejchan = find(~ismember([EEG.allchan.urchan], [EEG.chanlocs.urchan]));
  48. rejchan_one = setdiff({EEG.allchan.labels}, {EEG.chanlocs.labels}); % I think this is superior to the previous line
  49. fprintf('Rejected channel step 1: %s\n',rejchan_one{:}); % manually copy into excel file?
  51. %% Automated removal bad electrodes step 2
  52. EEG = pop_rejchan(EEG, 'elec', 1:size(EEG.data,1), 'threshold', 4, 'norm', 'on', 'measure', 'kurt'); % same for these parameters
  53. % EEG.rejchan = find(~ismember([EEG.allchan.urchan], [EEG.chanlocs.urchan]));
  54. rejchan_two = setdiff(setdiff({EEG.allchan.labels}, {EEG.chanlocs.labels}),rejchan_one);
  55. fprintf('Rejected channel step 2: %s\n',rejchan_two{:}); % manually copy into excel file?
  56. EEG.rejchan = [rejchan_one rejchan_two];
  58. % manual rejection of electrodes should be added here
  59. %% Fix latency of events in D2 and D10 condition (from marker on conditioning pulse to marker on test pulse)
  60. EEG.oldeventlatency = [EEG.event.latency];
  61. for i = 1:size(EEG.event,2)
  62. if EEG.event(i).type == 3
  63. EEG.event(i).latency = EEG.event(i).latency + 21;
  64. EEG.urevent(i).latency = EEG.urevent(i).latency + 21;
  65. elseif EEG.event(i).type == 5
  66. EEG.event(i).latency = EEG.event(i).latency + 101;
  67. EEG.urevent(i).latency = EEG.urevent(i).latency + 101;
  68. end
  69. end
  71. %% Epoch segmentation
  72. EEG = pop_epoch(EEG, {'1', '3', '5'}, [-1.5 1.5]);
  74. %% Baseline correction
  75. EEG = pop_rmbase(EEG, [-800 -110]);
  76. %pop_eegplot(EEG,1,1,1);
  78. %% Seperate epochs on condition
  79. EEG_SP = pop_select(EEG, 'trial', find([EEG.event.type] == 1));
  80. EEG_D2 = pop_select(EEG, 'trial', find([EEG.event.type] == 3));
  81. EEG_D10 = pop_select(EEG, 'trial', find([EEG.event.type] == 5));
  83. %% Save additional information for later checks
  84. EEG_SP.rawepochs = EEG_SP.epoch;
  85. EEG_SP.rawurevents = EEG_SP.urevent;
  87. EEG_D2.rawepochs = EEG_D2.epoch;
  88. EEG_D2.rawurevents = EEG_D2.urevent;
  90. EEG_D10.rawepochs = EEG_D10.epoch;
  91. EEG_D10.rawurevents = EEG_D10.urevent;
  93. %% Manually check for true presence of TMS-pulse at given marker SP
  94. EEG_pulsecheck_SP = epoch2continuous(EEG_SP);
  95. EEG_pulsecheck_SP = tesa_findpulse(EEG_pulsecheck_SP, 'CZ', 'refract', 10, 'rate', 2e4, 'tmsLabel', 'SP'); % CZ, but PZ can be used if CZ was already filtered out
  96. EEG_SP.pulseinfo = EEG_pulsecheck_SP.event;
  97. fprintf('Is number of single pulses detected equal to %d?\n',EEG_SP.trials);
  98. % pop_eegplot(EEG_pulsecheck_SP,1,1,1);
  99. % EEG_SP = pop_select(EEG_SP, 'notrial', [44:51])
  101. %% Manually check for true presence of TMS-pulse at given marker D2
  102. EEG_pulsecheck_D2 = epoch2continuous(EEG_D2);
  103. EEG_pulsecheck_D2 = tesa_findpulse(EEG_pulsecheck_D2, 'CZ', 'refract', 2, 'rate', 2e4, 'paired', 'yes', 'ISI', 2);
  104. EEG_D2.pulseinfo = EEG_pulsecheck_D2.event;
  105. fprintf('Is number of test pulses detected equal to %d?\n',EEG_D2.trials); % dit automatiseren (latencies vergelijken en epoch verwijderen waar niet overeen)
  106. % pop_eegplot(EEG_pulsecheck_D2,1,1,1);
  107. % EEG_D2 = pop_select(EEG_D2, 'notrial', [43:44]);
  109. %% Manually check for true presence of TMS-pulse at given marker D10
  110. EEG_pulsecheck_D10 = epoch2continuous(EEG_D10);
  111. EEG_pulsecheck_D10 = tesa_findpulse(EEG_pulsecheck_D10, 'CZ', 'refract', 10, 'rate', 2e4, 'paired', 'yes', 'ISI', 10);
  112. EEG_D10.pulseinfo = EEG_pulsecheck_D10.event;
  113. fprintf('Is number of test pulses detected equal to %d?\n',EEG_D10.trials);
  114. % pop_eegplot(EEG_pulsecheck_D10,1,1,1);
  115. % EEG_D10 = pop_select(EEG_D10, 'notrial', [21]);
  117. %% Number of raw epochs per condition
  118. fprintf('N_rawepochs (SP/D2/D10): %d/%d/%d\n', length(EEG_SP.epoch),... % klopt dit nu?
  119. length(EEG_D2.epoch), length(EEG_D10.epoch));
  121. %% Seperate epochs for conditions and save
  122. mkdir([DATAOUT, '/', ppn, '/', br])
  123. pop_saveset(EEG_SP, 'filename', [ppn, '_rawepochs_', br, '_SP.set'], 'filepath', [DATAOUT, ppn, '/', br]);
  124. pop_saveset(EEG_D2, 'filename', [ppn, '_rawepochs_', br, '_D2.set'], 'filepath', [DATAOUT, ppn, '/', br]);
  125. pop_saveset(EEG_D10, 'filename', [ppn, '_rawepochs_', br, '_D10.set'], 'filepath', [DATAOUT, ppn, '/', br]);

preprocessing_1.m at commit c9ba510, no license · at the source

Overview

Authors: Eva Oostra1,2,3,4, Emile d’Angremont2, Timo van Hattem5,6, Shilpa Anand7, Sophie Schubert8,9, Odile A van den Heuvel1,2,10, Ysbrand D van der Werf2,10
ORCID iDs: Eva Oostra
  1. Amsterdam UMC, Vrije Universiteit Amsterdam, Dept. Psychiatry, De Boelelaan 1117, Amsterdam, the Netherlands
  2. Amsterdam UMC, Vrije Universiteit Amsterdam, Dept Anatomy & Neuroscience, De Boelelaan 1117, Amsterdam, the Netherlands
  3. GGZ inGeest Specialized Mental Health Care, Amsterdam, the Netherlands
  4. Amsterdam Neuroscience, Mood, Anxiety, Psychosis, Sleep & Stress program, Amsterdam, the Netherlands
  5. Hertie-Institute for Clinical Brain Research, University of Tübingen, Germany
  6. Department of Neurology & Stroke, University of Tübingen, Germany
  7. Child and Adolescent Psychiatry and Psychosocial Care, Emma Children’s Hospital, Amsterdam UMC, Amsterdam, the Netherlands
  8. Brain Research and Innovation Centre, Ministry of Defence, the Netherlands
  9. Department of Psychiatry, University Medical Centre Utrecht Brain Centre, the Netherlands
  10. Amsterdam Neuroscience, Compulsivity Impulsivity Attention program, Amsterdam, the Netherlands
Institutions: Amsterdam Neuroscience (Netherlands); GGZ inGeest (Netherlands); Amsterdam University Medical Centers (Netherlands); Vrije Universiteit Amsterdam (Netherlands); Hertie Institute for Clinical Brain Research (Germany); University of Tübingen (Germany); Emma Kinderziekenhuis (Netherlands); University Medical Center Utrecht (Netherlands); Ministry of Defence (Netherlands)
Journal: Clinical neurophysiology practice, volume 11, pages 273-281
Dates: received 28 October 2025; accepted 31 March 2026; published online 4 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.cnp.2026.03.007 · PMID 42006918 · PMCID PMC13091408 · OpenAlex W7149358289
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), other (modality), human (organism), healthy (population), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, fMRI & imaging, Physiology & signal measures
Keywords: Transcranial magnetic stimulation, Electroencephalography, Prefrontal cortex, Motor cortex, Healthy subjects
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Dutch Research Council (NWO) (91717306); ZonMw (91717306)
Citations: not cited yet (Europe PMC); 23 references in the paper

Abstract

Background: Independent component analysis (ICA) is a common method to remove artifacts and improve signal quality in transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) data. However, the impact of applying different rounds of ICA for TMS-EEG datasets are lacking. Here, we investigated the impact of applying zero, one, or two rounds of ICA on TMS-evoked potentials (TEPs) in the motor cortex and prefrontal cortex using a within-subject design.

Methods: Twenty-three healthy participants received 51 single-pulses over the left primary motor cortex (M1) and dorsolateral prefrontal cortex (DLPFC), with simultaneous EEG-recording. Data were preprocessed three times: with zero, one, or two rounds of ICA. We compared the TEP-amplitudes and local mean field potential-area under the curve (LMFP-AUC).

Results: After M1 stimulation, preprocessing two ICA rounds resulted in significantly more N40-peaks identified, and smaller P30 (3.84 µV ± 0.78) and P60 (3.08 µV ± 0.96) amplitudes compared to 0 ICA rounds. After DLPFC stimulation, zero ICA rounds led to significantly fewer identified P30 (n = 4) and N40-peaks (n = 5), and larger P60 (5.63 µV ± 1.34) and P180-amplitudes (8.62 µV ± 0.76) compared to 0 ICA. The N100-component remained stable across ICA conditions for both brain areas.

Discussion: Our results showed a large impact of ICA, compared to zero ICA rounds, particularly after DLPFC stimulation; likely due to increased eye-blink artifacts. We therefore recommend implementing eye-blink artefact removal during the first round of ICA. In M1, the addition of an ICA round only impacted the early-TEPs. This study is the first to directly compare ICA-effects on both motor and non-motor TMS-EEG data using a within-subject design.

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 2 matches between paragraphs and lines of code.

emiledangremont/TMS-EEG

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: c9ba51000ce46e0d7ac31a3535142af365ea7643, 27 November 2023
Languages: MATLAB (2)
Size: 3 files, 2 scripts
Software Heritage: not archived
Found in: the text, “Preprocessing of acquired TMS-EEG data”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: EEGLAB (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
3 files
At the source: github.com/emiledangremont/TMS-EEG

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 7 authors, 5 keywords, 2 funders, 23 references.

Cite

This paper

Oostra, E., d’Angremont, E., van Hattem, T., Anand, S., Schubert, S., van den Heuvel, O. A., & van der Werf, Y. D. (2026). The impact of Independent Component Analysis on TMS-evoked potentials: a within-subject comparison across motor and prefrontal areas. Clinical neurophysiology practice, 11, 273-281. https://doi.org/10.1016/j.cnp.2026.03.007

BibTeX

@article{oostra2026impact,
author = {Oostra, Eva and d’Angremont, Emile and van Hattem, Timo and Anand, Shilpa and Schubert, Sophie and van den Heuvel, Odile A and van der Werf, Ysbrand D},
title = {{The impact of Independent Component Analysis on TMS-evoked potentials: a within-subject comparison across motor and prefrontal areas}},
journal = {Clinical neurophysiology practice},
year = {2026},
month = apr,
volume = {11},
pages = {273--281},
publisher = {Elsevier},
issn = {2467-981X},
doi = {10.1016/j.cnp.2026.03.007},
url = {https://doi.org/10.1016/j.cnp.2026.03.007},
pmid = {42006918},
pmcid = {PMC13091408}
}

RIS

TY - JOUR
AU - Oostra, Eva
AU - d’Angremont, Emile
AU - van Hattem, Timo
AU - Anand, Shilpa
AU - Schubert, Sophie
AU - van den Heuvel, Odile A
AU - van der Werf, Ysbrand D
TI - The impact of Independent Component Analysis on TMS-evoked potentials: a within-subject comparison across motor and prefrontal areas
T2 - Clinical neurophysiology practice
J2 - Clin Neurophysiol Pract
PY - 2026
DA - 2026/04/04
VL - 11
SP - 273
EP - 281
SN - 2467-981X
PB - Elsevier
DO - 10.1016/j.cnp.2026.03.007
UR - https://doi.org/10.1016/j.cnp.2026.03.007
LA - en
ER -

CSL-JSON

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"title": "The impact of Independent Component Analysis on TMS-evoked potentials: a within-subject comparison across motor and prefrontal areas",
"container-title": "Clinical neurophysiology practice",
"author": [
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"PMCID": "PMC13091408",
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