OSCR

Minimum data requirements and automated preprocessing for reliable EEG biomarkers in Rett syndrome.

Code ↔ Paper

9 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 9 matches
  1. [1] § Materials and methods › EEG processing › Correction-based preprocessing pipeline ↔ autoclave2_processing/atclv2_R61_preprocASR.m, lines 82–128 · score 0.76 · burst criterion, CLEAN_RAWDATA, window length, calibration, noise, EEGLAB
  2. [2] § Materials and methods › EEG processing › Correction-based preprocessing pipeline ↔ autoclave2_processing/atclv2_R61_preprocASR_bcrTest.m, lines 83–133 · score 0.76 · burst criterion, CLEAN_RAWDATA, window length, calibration, noise, EEGLAB
  3. [3] § Materials and methods › EEG processing › Correction-based preprocessing pipeline ↔ autoclave2_processing/atclv2_R61_screen.m, lines 57–164 · score 0.73 · LOF score, LOF threshold, good channels, adaptive, cutoff, SD
  4. [4] § Materials and methods › EEG processing › Correction-based preprocessing pipeline ↔ atclv2_main_R61_REST.m, lines 19–49 · score 0.70 · NEAR_ChannelReject, clean_rawdata, ICLabel, scd, EEGLAB
  5. [5] § Materials and methods › EEG processing › Correction-based preprocessing pipeline ↔ autoclave2_processing/atclv2_R61_preprocLOF.m, lines 86–118 · score 0.68 · Local Outlier Factor, Bad channel rejection, flatline, LOF, threshold, preprocessing
  6. [6] § Materials and methods › EEG processing › Correction-based preprocessing pipeline ↔ atclv2_main_R61_REST.m, lines 111–124 · score 0.54 · Bad channel rejection, CLEAN_RAWDATA, temporal, preprocessing
  7. [7] § Materials and methods › EEG processing › Feature extraction ↔ autoclave2_processing/atclv2_R61_featExt.m, lines 84–170 · score 0.52 · Power spectral, theta, beta, alpha, gamma, delta
  8. [8] § Materials and methods › EEG processing › Feature extraction ↔ autoclave2_processing/atclv2_R61_featExtComp.m, lines 168–254 · score 0.52 · Power spectral, theta, beta, alpha, gamma, delta
  9. [9] § Materials and methods › EEG processing › Correction-based preprocessing pipeline ↔ autoclave2_processing/atclv2_R61_preprocLOF.m, lines 86–118 · score 0.51 · Local Outlier Factor, LOF, metrics, channels, preprocessing, threshold

Paper

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The authors' code

MATLAB · 304 lines · 10 KB · no license · 2 matches

  1. % =====================================
  2. % ============ Autoclave2 =============
  3. % =====================================
  5. % Version: Rett Minimum Time Study
  7. % Author: Yongtaek Oh Ph.D., Department of Pediatrics, Children's Hospital of Philadelphia
  8. % Based on Brian Erickson's Autoclave
  10. % Fully automatic pipeline based on Makoto Miyakoshi (UCSD)'s recommendation
  11. % (https://sccn.ucsd.edu/wiki/Makoto's_preprocessing_pipeline) on processing order
  13. % Developed under Matlab R2023a and EEGLAB 2024.2
  16. %% Dev note
  19. %% README!!!!
  21. % 1. Your project folder should be structured in the following hierarchy:
  22. % - Project Folder
  23. % * R61_REST
  24. % L Step folder (e.g. R61Rest_a_raw)
  25. L Subject Folder (e.g. 6101-01)
  26. L File (e.g. sub-6101-01_ses-BASELINE_task-REST_eeg.set)
  27. % * utilities (The name 'utilities' is hard-coded, so use the exact name)
  29. % 2. Create 'utilities' folder in your project folder if not present
  30. %
  31. % 3. Set your project folder as working directory
  32. %
  33. % 4. You should have EEGLAB
  34. % - Download EEGLAB: https://sccn.ucsd.edu/eeglab/download.php
  35. %
  36. % 5. Run EEGLAB by typing 'eeglab' in the command window, and click File -> Manage EEGLAB extensions
  37. %
  38. % 6. Add the following EEGLAB extensions:
  39. % - Appropriate import tool (e.g. Neuroscanio for Neuroscan files)
  40. % - NEAR_ChannelReject
  41. % - clean_rawdata (available by default in recent EEGLAB versions)
  42. % - ICLabel (available by default in recent EEGLAB versions)
  43. % - scd
  44. %
  45. % 7. Change the setting to use double precision (not necessary for EEGLAB
  46. % version 2024)
  47. % - File -> Memory and other Options -> First checkbox under "Memory Options"
  48. %
  49. % 8. Now it's good to go!!
  52. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  54. %% IMPORT, FILTER, AND AVERAGE REFERENCE
  56. param.rename = {'F10', 1; 'AF8', 2; 'AF4', 3; 'F2', 4;
  57. 'FCz', 6; 'Fp2', 9; 'Fz', 11; 'FC1', 13;
  58. 'FPz', 15; 'AFz', 16; 'F1', 19; 'Fp1', 22;
  59. 'AF3', 23; 'F3', 24; 'AF7', 26; 'F5', 27; 'FC5', 28;
  60. 'FC3', 29; 'C1', 30; 'F9', 32; 'F7', 33;
  61. 'FT7', 34; 'C3', 36; 'CP1', 37; 'FT9', 38;
  62. 'C5', 41; 'CP3', 42; 'T9', 44; 'T7', 45;
  63. 'Tp7', 46; 'CP5', 47; 'P5', 51; 'P3', 52;
  64. 'CPz', 55; 'TP9', 57; 'P7', 58; 'P1', 60;
  65. 'Pz', 62; 'P9', 64; 'PO7', 65; 'PO3', 67; 'O1', 70; 'POz', 72;
  66. 'Oz', 75; 'PO4', 77; 'O2', 83; 'P2', 85;
  67. 'CP2', 87; 'PO8', 90; 'P4', 92; 'CP4', 93; 'P10', 95; 'P8', 96;
  68. 'P6', 97; 'CP6', 98; 'TP10', 100; 'TP8', 102; 'C6', 103;
  69. 'C4', 104; 'C2', 105; 'T8', 108;
  70. 'FC4', 111; 'FC2', 112; 'T10', 114; 'FT8', 116;
  71. 'FC6', 117; 'FT10', 121; 'F8', 122; 'F6', 123;
  72. 'F4', 124; 'Cz', 129};
  74. param.inFolder = 'R61REST_a_raw_';
  75. param.outFolder = 'R61REST_b_prep_filt';
  76. param.fileType = ['*' filesep '*.set']; % file extension to look for
  78. param.projName = 'R61TEST_71';
  80. param.locutoff = 1; % Hz Cutoff for high-pass filter
  81. param.hicutoff = 80; % Hz Cutoff for low-pass filter
  82. param.notch = 60;
  84. %param.unused = {'E17', 'E125', 'E126', 'E127', 'E128', 'ECG'};
  86. param.resampleRate = 500; % Sampling frequency to downsample
  87. param.reref = 'average';
  89. funct = {@atclv2_R61_preprocFilt}; % cell of @functionHandles
  90. fullReport = atclv2_masterSelector(param,funct,...
  91. 'auto',1,'save',1,'vol',1,'global',1);
  93. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  95. %% PERFORM BAD CHANNEL REJECTION USING LOF METHOD
  97. param.inFolder = 'R61REST_b_prep_filt';
  98. param.outFolder = 'R61REST_c_prep_LOF';
  99. param.fileType = '*.set'; % file extension to look for
  101. param.LOFflatLine = 5;
  102. param.LOFthresh = 2.5;
  103. param.LOFadapt = 10;
  105. funct = {@atclv2_R61_preprocLOF}; % cell of @functionHandles
  106. fullReport = atclv2_masterSelector(param,funct,...
  107. 'auto',1,'save',1,'vol',1,'global',1);
  109. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  111. %% TEMPORARY - PERFORM BAD CHANNEL REJECTION USING CLEAN_RAWDATA
  113. param.inFolder = 'R61REST_b_prep_filt';
  114. param.outFolder = 'R61REST_c_prep_CR';
  115. param.fileType = '*.set'; % file extension to look for
  117. param.flatChan = 5;
  118. param.chanCr = 0.85;
  119. param.asrSD = 'off';
  120. param.winCr = 'off';
  122. funct = {@atclv2_R61_preprocASR_bcrTest}; % cell of @functionHandles
  123. fullReport = atclv2_masterSelector(param,funct,...
  124. 'auto',1,'save',1,'vol',1,'global',1);
  126. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  128. %% APPLY CLEAN_RAWDATA (ASR)
  130. param.inFolder = 'R61REST_c_prep_LOF';
  131. param.outFolder = 'R61REST_d_prep_ASR';
  132. param.fileType = '*.set'; % file extension to look for
  134. param.asrSD = 25;
  135. param.winCr = 0.25;
  137. param.windowLen = [];
  138. param.refWndLen = [];
  139. param.refMaxBC = [];
  141. funct = {@atclv2_R61_preprocASR}; % cell of @functionHandles
  142. fullReport = atclv2_masterSelector(param,funct,...
  143. 'auto',1,'save',1,'vol',1,'global',1);
  145. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  147. %% POST-ASR processing, ICA
  149. param.inFolder = 'R61REST_d_prep_ASR';
  150. param.outFolder = 'R61REST_e_ICA';
  151. param.fileType = '*.set'; % file extension to look for.
  153. funct = {@atclv2_R61_postASRICA}; % cell of @functionHandles
  154. fullReport = atclv2_masterSelector(param,funct,...
  155. 'auto',1,'save',0,'vol',1,'global',1);
  157. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  159. %% Label artifactual components
  161. param.markMethod = 'ICLabel'; %'ADJUST' or 'ICLabel'
  163. param.inFolder = 'R61REST_e_ICA';
  164. param.outFolder = 'R61REST_f_markComps_ICA_ICLabel';
  165. param.fileType = '*.set'; % file extension to look for.
  167. % ICLabel parameters
  168. % Min Max
  169. param.rejThreshold = [0 0; %Brain
  170. 0.75 1; %Muscle
  171. 0.75 1; %Eye
  172. 0 0; %Heart
  173. 0 0; %Line noise
  174. 0 0; %Channel noise
  175. 0 0]; %Other
  177. param.artSumThresh = 1;
  179. funct = {@atclv2_R61_ICMarkComps}; % cell of @functionHandles
  180. fullReport = atclv2_masterSelector(param,funct,...
  181. 'auto',1,'save',0,'vol',1,'global',1);
  183. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  185. %% Reject marked ICs
  187. param.inFolder = 'R61REST_f_markComps_ICA_ICLabel';
  188. param.outFolder = 'R61REST_g_rejComps_ICA_ICLabel';
  189. param.fileType = '*.set'; % file extension to look for.
  191. param.artSumThresh = 1;
  192. param.markMethod = 'ICLabel';
  194. funct = {@atclv2_R61_ICLabelReject}; % cell of @functionHandles
  195. fullReport = atclv2_masterSelector(param,funct,...
  196. 'auto',1,'save',0,'vol',1,'global',1);
  198. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  200. %% Post-processing and Regular Epoching
  202. param.inFolder = 'R61REST_g_rejComps_ICA_ICLabel';
  203. param.outFolder = 'R61REST_h_postproc_ICA_ICLabel';
  204. param.fileType = '*.set'; % file extension to look for.
  206. param.hicutoff = 50;
  207. param.winCr = 'off';
  208. param.rotateNosedir = 1;
  210. funct = {@atclv2_R61_postproc}; % cell of @functionHandles
  211. fullReport = atclv2_masterSelector(param,funct,...
  212. 'auto',1,'save',0,'vol',1,'global',1);
  214. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  216. %% Apply Hjorth Laplacian
  218. param.inFolder = 'R61REST_h_postproc_ICA_ICLabel';
  219. param.outFolder = 'R61REST_i_postproc_lap';
  220. param.fileType = '*.set'; % file extension to look for.
  222. param.lapMethod = 'hjorth';
  224. funct = {@atclv2_R61_laplacian}; % cell of @functionHandles
  225. fullReport = atclv2_masterSelector(param,funct,...
  226. 'auto',1,'save',0,'vol',1,'global',1);
  227. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  229. % %% Feature extraction - for feature comparison with original cleaning
  230. %
  231. % param.inFolder = 'R61REST_i_postproc_lap';
  232. % param.outFolder = 'R61REST_z_featComp';
  233. % param.fileType = '*.set'; % file extension to look for.
  234. %
  235. % % param.chanSel = {'E22', 'Fp1';
  236. % % 'E9', 'Fp2';
  237. % % 'E33', 'F7';
  238. % % 'E24', 'F3';
  239. % % 'E11', 'Fz';
  240. % % 'E124', 'F4';
  241. % % 'E122', 'F8';
  242. % % 'E44', 'T9';
  243. % % 'E40', 'T7';
  244. % % 'E41', 'C5';
  245. % % 'E36', 'C3';
  246. % % 'E30', 'C1';
  247. % % 'E105', 'C2';
  248. % % 'E104', 'C4';
  249. % % 'E103', 'C6';
  250. % % 'E109', 'T8';
  251. % % 'E114', 'T10';
  252. % % 'E52', 'P5';
  253. % % 'E61', 'P1';
  254. % % 'E62', 'Pz';
  255. % % 'E78', 'P2';
  256. % % 'E92', 'P6';
  257. % % 'E59', 'P7';
  258. % % 'E60', 'P3';
  259. % % 'E85', 'P4';
  260. % % 'E91', 'P8';
  261. % % 'E70', 'O1';
  262. % % 'E75', 'Oz';
  263. % % 'E83', 'O2'};
  264. %
  265. % param.regEpoch = 4; % 0 if no epoching, or number of seconds to epoch
  266. % param.retainBound = 0;
  267. %
  268. % funct = {@atclv2_R61_featExtComp}; % cell of @functionHandles
  269. % fullReport = atclv2_masterSelector(param,funct,...
  270. % 'auto',1,'save',0,'vol',1,'global',1);
  272. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  274. %% Feature extraction
  276. param.inFolder = 'R61REST_i_postproc_lap';
  277. param.outFolder = 'R61REST_j_featExt_lap';
  278. param.fileType = '*.set'; % file extension to look for.
  280. param.regEpoch = 4; % 0 if no epoching, or number of seconds to epoch
  281. param.retainBound = 0;
  283. funct = {@atclv2_R61_featExt}; % cell of @functionHandles
  284. fullReport = atclv2_masterSelector(param,funct,...
  285. 'auto',1,'save',0,'vol',1,'global',1);
  287. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  289. %% Screen
  291. param.inFolder = 'R61REST_h_postproc_ICA_ICLabel';
  292. param.outFolder = [param.inFolder '_screen'];
  293. param.fileType = '*.set'; % file extension to look for.
  295. param.sd = 3;
  296. param.rejPercent = 0.5; % Reject if less than this percentage of data remains
  298. funct = {@atclv2_R61_screen}; % cell of @functionHandles
  299. fullReport = atclv2_masterSelector(param,funct,...
  300. 'auto',1,'save',0,'vol',1,'global',1);
  302. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  304. % Use stability_featExt.m to do feature extraction

atclv2_main_R61_REST.m at commit dfcb427, no license · at the source

Overview

Authors: Yongtaek Oh1, Kathleen Campbell2, Justine Shults3,4, Joni Saby1, Eric D Marsh1,3,5
ORCID iDs: Yongtaek Oh
  1. Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, United States
  2. Division of Developmental and Behavioral Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA, United States
  3. Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
  4. Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
  5. Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
Institutions: Children's Hospital of Philadelphia (United States); University of Pennsylvania (United States)
Journal: Frontiers in neurology, volume 17, article 1791834
Dates: received 20 January 2026; accepted 22 May 2026; published online 16 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fneur.2026.1791834 · PMID 42383019 · PMCID PMC13314485 · OpenAlex W7164885236
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), other condition (population), clinical / translational (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, Physiology & signal measures
Keywords: automated preprocessing, biomarkers, data sufficiency, EEG, Rett syndrome
Topic: Genetics and Neurodevelopmental Disorders (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Citations: not cited yet (Europe PMC); 39 references in the paper

Abstract

Background: Electroencephalography (EEG) is a promising biomarker for Rett syndrome (RTT), but excessive artifact and variable tolerance for longer recording sessions pose challenges for reliable biomarker development. Establishing an automated preprocessing pipeline that matches human review and defining the minimum data needed for stable quantitative EEG (qEEG) features can support more patient-friendly protocols and provide consistent multisite analysis results.

Methods: A mean of 10 min of resting-state EEG from 117 participants (1–18 year old; 236 sessions) in the multisite R61 RTT study was processed using a fully automated, correction-based preprocessing pipeline incorporating artifact handling, adaptive channel rejection, ASR, and ICA-based cleaning. Spectral power was extracted from artifact-free 4-s epochs. The proposed pipeline is validated using an established rejection-based pipeline. Feature stability as a function of cumulative data length was then assessed using two complementary frameworks: a Statistical Convergence approach and a Model-Based Inflection approach, and potential systemic dependencies were evaluated using permutation analyses. The relationship between clinical measures was also assessed.

Results: The correction-based pipeline retained substantially more data than the rejection-based workflow (mean retention = 95.0% vs. 28.4%; p < 0.001) while preserving strong feature correspondence across frequency bands. Stable power estimates were achieved after 19–34 epochs (= 76–136 s). Based on permutation analysis, there was no statistically significant difference in minimum stabilization threshold between RTT and TD. However, the RTT group exhibited higher rates of intrinsic signal instability than typically developing (TD) controls. Age-stratified analysis revealed that the minimum epochs did not significantly differ between age groups. Spectral associations with clinical severity were preserved when using only the minimum data required for stability, as well as in an ecologically valid scenario of truncating the raw EEG up to minimum epoch recommendation and reprocessing it.

Conclusions: With the proposed correction-based pipeline, approximately 3 min of raw resting-state EEG are sufficient to obtain stable and clinically meaningful spectral features in children with Rett syndrome. These findings support shorter, more feasible EEG acquisitions and provide a reproducible framework for data sufficiency in multisite neurodevelopmental studies.

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 9 matches between paragraphs and lines of code.

ytoh2000/Rett_MinimumTime

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: dfcb427221eb05270d079cf7fe1c4f376a137927, 29 December 2025
Languages: MATLAB (24)
Size: 27 files, 24 scripts
Software Heritage: not archived
Found in: the text, “Correction-based preprocessing pipeline”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: EEGLAB (14 files), FieldTrip (1 file), ICLabel (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
24 files
At the source: github.com/ytoh2000/Rett_MinimumTime

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;
  • 24 scripts, each with its path and the digest of its content;
  • 9 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

No dataset and no data link were found in the paper.

Data availability statement

The data analyzed in this study is subject to the following licenses/restrictions: The data that support the findings of this study are not openly available but are available upon reasonable request from the corresponding author subject to IRB and governance approvals. Requests to access these datasets should be directed to Eric D. Marsh, .

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 5 keywords, 34 references.

Cite

This paper

Oh, Y., Campbell, K., Shults, J., Saby, J., & Marsh, E. D. (2026). Minimum data requirements and automated preprocessing for reliable EEG biomarkers in Rett syndrome. Frontiers in neurology, 17, 1791834. https://doi.org/10.3389/fneur.2026.1791834

BibTeX

@article{oh2026minimum,
author = {Oh, Yongtaek and Campbell, Kathleen and Shults, Justine and Saby, Joni and Marsh, Eric D},
title = {{Minimum data requirements and automated preprocessing for reliable EEG biomarkers in Rett syndrome}},
journal = {Frontiers in neurology},
year = {2026},
month = jun,
volume = {17},
pages = {1791834},
publisher = {Frontiers Media SA},
issn = {1664-2295},
doi = {10.3389/fneur.2026.1791834},
url = {https://doi.org/10.3389/fneur.2026.1791834},
pmid = {42383019},
pmcid = {PMC13314485}
}

RIS

TY - JOUR
AU - Oh, Yongtaek
AU - Campbell, Kathleen
AU - Shults, Justine
AU - Saby, Joni
AU - Marsh, Eric D
TI - Minimum data requirements and automated preprocessing for reliable EEG biomarkers in Rett syndrome
T2 - Frontiers in neurology
J2 - Front Neurol
PY - 2026
DA - 2026/06/16
VL - 17
SP - 1791834
SN - 1664-2295
PB - Frontiers Media SA
DO - 10.3389/fneur.2026.1791834
UR - https://doi.org/10.3389/fneur.2026.1791834
LA - en
ER -

CSL-JSON

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"id": "10.3389/fneur.2026.1791834",
"type": "article-journal",
"title": "Minimum data requirements and automated preprocessing for reliable EEG biomarkers in Rett syndrome",
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"author": [
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"given": "Yongtaek"
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{
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{
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"given": "Eric D"
}
],
"container-title-short": "Front Neurol",
"volume": "17",
"page": "1791834",
"DOI": "10.3389/fneur.2026.1791834",
"PMID": "42383019",
"PMCID": "PMC13314485",
"ISSN": "1664-2295",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fneur.2026.1791834",
"language": "en",
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2026,
6,
16
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]
}
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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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[9] doi:10.3758/s13428-026-02997-z [code]
PyLossless: A non-destructive EEG processing pipeline.
Journal: Behavior research methods
In common: ICLabel, EEG, 3 references
[10] doi:10.1111/ejn.70255 [code]
A Systematic Review of Aperiodic Neural Activity in Clinical Investigations
Journal: n/a
In common: EEG, clinical / translational, 3 references

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