{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T16:26:00Z","timestamp":1773159960260,"version":"3.50.1"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T00:00:00Z","timestamp":1715731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neuroinform."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>At the intersection of neural monitoring and decoding, event-related potential (ERP) based on electroencephalography (EEG) has opened a window into intrinsic brain function. The stability of ERP makes it frequently employed in the field of neuroscience. However, project-specific custom code, tracking of user-defined parameters, and the large diversity of commercial tools have limited clinical application.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We introduce an open-source, user-friendly, and reproducible MATLAB toolbox named EPAT that includes a variety of algorithms for EEG data preprocessing. It provides EEGLAB-based template pipelines for advanced multi-processing of EEG, magnetoencephalography, and polysomnogram data. Participants evaluated EEGLAB and EPAT across 14 indicators, with satisfaction ratings analyzed using the Wilcoxon signed-rank test or paired t-test based on distribution normality.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>EPAT eases EEG signal browsing and preprocessing, EEG power spectrum analysis, independent component analysis, time-frequency analysis, ERP waveform drawing, and topological analysis of scalp voltage. A user-friendly graphical user interface allows clinicians and researchers with no programming background to use EPAT.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This article describes the architecture, functionalities, and workflow of the toolbox. The release of EPAT will help advance EEG methodology and its application to clinical translational studies.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fninf.2024.1384250","type":"journal-article","created":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T05:16:34Z","timestamp":1715750194000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["EPAT: a user-friendly MATLAB toolbox for EEG\/ERP data processing and analysis"],"prefix":"10.3389","volume":"18","author":[{"given":"Jianwei","family":"Shi","sequence":"first","affiliation":[]},{"given":"Xun","family":"Gong","sequence":"additional","affiliation":[]},{"given":"Ziang","family":"Song","sequence":"additional","affiliation":[]},{"given":"Wenkai","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Yanfeng","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Xiangjie","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Penghu","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Changming","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Guoguang","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,5,15]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1684\/epd.2020.1217","article-title":"Electroencephalography: basic biophysical and technological aspects important for clinical applications","volume":"22","author":"Beniczky","year":"2020","journal-title":"Epileptic Disord."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"42","DOI":"10.3389\/fninf.2016.00042","article-title":"Hierarchical event descriptors (HED): semi-structured tagging for real-world events in large-scale EEG","volume":"10","author":"Bigdely-Shamlo","year":"2016","journal-title":"Front. 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