{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:48:03Z","timestamp":1775069283032,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T00:00:00Z","timestamp":1628726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute of Information &amp; Communications Technology (IITP)","award":["2017-0-00451"],"award-info":[{"award-number":["2017-0-00451"]}]},{"name":"Institute of Information &amp; Communications Technology (IITP)","award":["2019-0-01842"],"award-info":[{"award-number":["2019-0-01842"]}]},{"name":"Institute of Information &amp; Communications Technology (IITP)","award":["2021-0-01537"],"award-info":[{"award-number":["2021-0-01537"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Brain\u2013computer interfaces (BCIs) facilitate communication for people who cannot move their own body. A BCI system requires a lengthy calibration phase to produce a reasonable classifier. To reduce the duration of the calibration phase, it is natural to attempt to create a subject-independent classifier with all subject datasets that are available; however, electroencephalogram (EEG) data have notable inter-subject variability. Thus, it is very challenging to achieve subject-independent BCI performance comparable to subject-specific BCI performance. In this study, we investigate the potential for achieving better subject-independent motor imagery BCI performance by conducting comparative performance tests with several selective subject pooling strategies (i.e., choosing subjects who yield reasonable performance selectively and using them for training) rather than using all subjects available. We observed that the selective subject pooling strategy worked reasonably well with public MI BCI datasets. Finally, based upon the findings, criteria to select subjects for subject-independent BCIs are proposed here.<\/jats:p>","DOI":"10.3390\/s21165436","type":"journal-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T10:54:41Z","timestamp":1628765681000},"page":"5436","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Selective Subject Pooling Strategy to Improve Model Generalization for a Motor Imagery BCI"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5707-0304","authenticated-orcid":false,"given":"Kyungho","family":"Won","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5452-166X","authenticated-orcid":false,"given":"Moonyoung","family":"Kwon","sequence":"additional","affiliation":[{"name":"Korea Research Institute of Standards and Science, Safety Measurement Institute, Daejeon 34113, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6512-0167","authenticated-orcid":false,"given":"Minkyu","family":"Ahn","sequence":"additional","affiliation":[{"name":"School of Computer Science and Electrical Engineering, Handong Global University, Pohang 37554, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5357-4436","authenticated-orcid":false,"given":"Sung Chan","family":"Jun","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1016\/S1388-2457(02)00057-3","article-title":"Brain\u2013computer interfaces for communication and control","volume":"113","author":"Wolpaw","year":"2002","journal-title":"Clin. 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