{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/iclabel-an-automated-electroencephalographic","title":"ICLabel: An automated electroencephalographic independent component classifier, dataset, and website","arxiv_id":"1901.07915","date":"2019-01-22","proceeding":null,"authors":["Luca Pion-Tonachini","Ken Kreutz-Delgado","Scott Makeig"],"abstract":"The electroencephalogram (EEG) provides a non-invasive, minimally\nrestrictive, and relatively low cost measure of mesoscale brain dynamics with\nhigh temporal resolution. Although signals recorded in parallel by multiple,\nnear-adjacent EEG scalp electrode channels are highly-correlated and combine\nsignals from many different sources, biological and non-biological, independent\ncomponent analysis (ICA) has been shown to isolate the various source generator\nprocesses underlying those recordings. Independent components (IC) found by ICA\ndecomposition can be manually inspected, selected, and interpreted, but doing\nso requires both time and practice as ICs have no particular order or intrinsic\ninterpretations and therefore require further study of their properties.\nAlternatively, sufficiently-accurate automated IC classifiers can be used to\nclassify ICs into broad source categories, speeding the analysis of EEG studies\nwith many subjects and enabling the use of ICA decomposition in near-real-time\napplications. While many such classifiers have been proposed recently, this\nwork presents the ICLabel project comprised of (1) an IC dataset containing\nspatiotemporal measures for over 200,000 ICs from more than 6,000 EEG\nrecordings, (2) a website for collecting crowdsourced IC labels and educating\nEEG researchers and practitioners about IC interpretation, and (3) the\nautomated ICLabel classifier. The classifier improves upon existing methods in\ntwo ways: by improving the accuracy of the computed label estimates and by\nenhancing its computational efficiency. The ICLabel classifier outperforms or\nperforms comparably to the previous best publicly available method for all\nmeasured IC categories while computing those labels ten times faster than that\nclassifier as shown in a rigorous comparison against all other publicly\navailable EEG IC classifiers.","url_abs":"http://arxiv.org/abs/1901.07915v2","url_pdf":"http://arxiv.org/pdf/1901.07915v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"iclabel-an-automated-electroencephalographic","repo_url":"https://github.com/lucapton/ICLabel-Train","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"}],"methods":[{"method_slug":"ica","method_name":"ICA"}],"datasets_introduced":[{"slug":"iclabel","name":"ICLabel","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.07915","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.07915"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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