{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T19:03:39Z","timestamp":1777489419948,"version":"3.51.4"},"reference-count":61,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"vor","delay-in-days":15,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000896","name":"Coleman Fung Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000896","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sinclair Foundation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Subject training is crucial for acquiring brain\u2013computer interface (BCI) control. Typically, this requires collecting user-specific calibration data due to high inter-subject neural variability that limits the usability of generic decoders. However, calibration is cumbersome and may produce inadequate data for building decoders, especially with na\u00efve subjects. Here, we show that a decoder trained on the data of a single expert is readily transferrable to inexperienced users via domain adaptation techniques allowing calibration-free BCI training. We introduce two real-time frameworks, (i) Generic Recentering (GR) through unsupervised adaptation and (ii) Personally Assisted Recentering (PAR) that extends GR by employing supervised recalibration of the decoder parameters. We evaluated our frameworks on 18 healthy na\u00efve subjects over five online sessions, who operated a customary synchronous bar task with continuous feedback and a more challenging car racing game with asynchronous control and discrete feedback. We show that along with improved task-oriented BCI performance in both tasks, our frameworks promoted subjects\u2019 ability to acquire individual BCI skills, as the initial neurophysiological control features of an expert subject evolved and became subject specific. Furthermore, those features were task-specific and were learned in parallel as participants practiced the two tasks in every session. Contrary to previous findings implying that supervised methods lead to improved online BCI control, we observed that longitudinal training coupled with unsupervised domain matching (GR) achieved similar performance to supervised recalibration (PAR). Therefore, our presented frameworks facilitate calibration-free BCIs and have immediate implications for broader populations\u2014such as patients with neurological pathologies\u2014who might struggle to provide suitable initial calibration data.<\/jats:p>","DOI":"10.1093\/pnasnexus\/pgae076","type":"journal-article","created":{"date-parts":[[2024,2,17]],"date-time":"2024-02-17T08:46:55Z","timestamp":1708159615000},"source":"Crossref","is-referenced-by-count":38,"title":["Transfer learning promotes acquisition of individual BCI skills"],"prefix":"10.1093","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4817-1768","authenticated-orcid":false,"given":"Satyam","family":"Kumar","sequence":"first","affiliation":[{"name":"Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin , Austin, TX 78712 , 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USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5819-1522","authenticated-orcid":false,"given":"Jos\u00e9 del R","family":"Mill\u00e1n","sequence":"additional","affiliation":[{"name":"Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin , Austin, TX 78712 , USA"},{"name":"Department of Neurology, The University of Texas at Austin , Austin, TX 78712 , USA"},{"name":"Mulva Clinic for the Neurosciences, The University of Texas at Austin , Austin, TX 78712 , USA"},{"name":"Departement of Biomedical Engineering, The University of Texas at Austin , Austin, TX 78712 , USA"}]}],"member":"286","published-online":{"date-parts":[[2024,2,16]]},"reference":[{"issue":"1","key":"2024040313392094500_pgae076-B1","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1002\/ana.23879","article-title":"Brain-machine interface in chronic stroke rehabilitation: a controlled study","volume":"74","author":"Ramos-Murguialday","year":"2013","journal-title":"Ann 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