{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T12:50:08Z","timestamp":1761396608098,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T00:00:00Z","timestamp":1694131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006769","name":"Russian Science Foundation","doi-asserted-by":"publisher","award":["22-19-00528"],"award-info":[{"award-number":["22-19-00528"]}],"id":[{"id":"10.13039\/501100006769","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Bayesian neural networks (BNNs) are effective tools for a variety of tasks that allow for the estimation of the uncertainty of the model. As BNNs use prior constraints on parameters, they are better regularized and less prone to overfitting, which is a serious issue for brain\u2013computer interfaces (BCIs), where typically only small training datasets are available. Here, we tested, on the BCI Competition IV 2a motor imagery dataset, if the performance of the widely used, effective neural network classifiers EEGNet and Shallow ConvNet can be improved by turning them into BNNs. Accuracy indeed was higher, at least for a BNN based on Shallow ConvNet with two of three tested prior distributions. We also assessed if BNN-based uncertainty estimation could be used as a tool for out-of-domain (OOD) data detection. The OOD detection worked well only in certain participants; however, we expect that further development of the method may make it work sufficiently well for practical applications.<\/jats:p>","DOI":"10.3390\/a16090429","type":"journal-article","created":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T07:57:17Z","timestamp":1694159837000},"page":"429","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Bayesian Opportunities for Brain\u2013Computer Interfaces: Enhancement of the Existing Classification Algorithms and Out-of-Domain Detection"],"prefix":"10.3390","volume":"16","author":[{"given":"Egor I.","family":"Chetkin","sequence":"first","affiliation":[{"name":"MEG Center, Moscow State University of Psychology and Education, 123290 Moscow, Russia"},{"name":"Institute of Nano-, Bio-, Information, Cognitive and Socio-Humanistic Sciences and Technologies, Moscow Institute of Physics and Technology, 123098 Moscow, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3257-1022","authenticated-orcid":false,"given":"Sergei L.","family":"Shishkin","sequence":"additional","affiliation":[{"name":"MEG Center, Moscow State University of Psychology and Education, 123290 Moscow, Russia"}]},{"given":"Bogdan L.","family":"Kozyrskiy","sequence":"additional","affiliation":[{"name":"Independent Researcher, 59000 Lille, France"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"031005","DOI":"10.1088\/1741-2552\/aab2f2","article-title":"A review of classification algorithms for EEG-based brain\u2013computer interfaces: A 10 year update","volume":"15","author":"Lotte","year":"2018","journal-title":"J. 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