{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T20:30:33Z","timestamp":1770755433521,"version":"3.50.0"},"reference-count":53,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T00:00:00Z","timestamp":1713312000000},"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. Robot. AI"],"abstract":"<jats:p>We introduce a novel approach to training data augmentation in brain\u2013computer interfaces (BCIs) using neural field theory (NFT) applied to EEG data from motor imagery tasks. BCIs often suffer from limited accuracy due to a limited amount of training data. To address this, we leveraged a corticothalamic NFT model to generate artificial EEG time series as supplemental training data. We employed the BCI competition IV \u20182a\u2019 dataset to evaluate this augmentation technique. For each individual, we fitted the model to common spatial patterns of each motor imagery class, jittered the fitted parameters, and generated time series for data augmentation. Our method led to significant accuracy improvements of over 2% in classifying the \u201ctotal power\u201d feature, but not in the case of the \u201cHiguchi fractal dimension\u201d feature. This suggests that the fit NFT model may more favorably represent one feature than the other. These findings pave the way for further exploration of NFT-based data augmentation, highlighting the benefits of biophysically accurate artificial data.<\/jats:p>","DOI":"10.3389\/frobt.2024.1362735","type":"journal-article","created":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T04:41:17Z","timestamp":1713328877000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Recruiting neural field theory for data augmentation in a motor imagery brain\u2013computer interface"],"prefix":"10.3389","volume":"11","author":[{"given":"Daniel","family":"Polyakov","sequence":"first","affiliation":[]},{"given":"Peter A.","family":"Robinson","sequence":"additional","affiliation":[]},{"given":"Eli J.","family":"Muller","sequence":"additional","affiliation":[]},{"given":"Oren","family":"Shriki","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,4,17]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.jneumeth.2015.06.002","article-title":"Physiologically based arousal state estimation and dynamics","volume":"253","author":"Abeysuriya","year":"2015","journal-title":"J. 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