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Syst."],"published-print":{"date-parts":[[2021,11]]},"abstract":"<jats:p>A significant problem in brain\u2013computer interface (BCI) research is decoding \u2014 obtaining required information from very weak noisy electroencephalograph signals and extracting considerable information from limited data. Traditional intention decoding methods, which obtain information from induced or spontaneous brain activity, have shortcomings in terms of performance, computational expense and usage burden. Here, a new methodology called prediction error decoding was used for motor imagery (MI) detection and compared with direct intention decoding. Galvanic vestibular stimulation (GVS) was used to induce subliminal sensory feedback between the forehead and mastoids without any burden. Prediction errors were generated between the GVS-induced sensory feedback and the MI direction. The corresponding prediction error decoding of the front\/back MI task was validated. A test decoding accuracy of 77.83\u201378.86% (median) was achieved during GVS for every 100[Formula: see text]ms interval. A nonzero weight parameter-based channel screening (WPS) method was proposed to select channels individually and commonly during GVS. When the WPS common-selected mode was compared with the WPS individual-selected mode and a classical channel selection method based on correlation coefficients (CCS), a satisfactory decoding performance of the selected channels was observed. The results indicated the positive impact of measuring common specific channels of the BCI.<\/jats:p>","DOI":"10.1142\/s0129065721500349","type":"journal-article","created":{"date-parts":[[2021,8,11]],"date-time":"2021-08-11T06:12:47Z","timestamp":1628662367000},"source":"Crossref","is-referenced-by-count":5,"title":["Galvanic Vestibular Stimulation-Based Prediction Error Decoding and Channel Optimization"],"prefix":"10.1142","volume":"31","author":[{"given":"Yuxi","family":"Shi","sequence":"first","affiliation":[{"name":"School of Engineering, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo, 152-8550, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gowrishankar","family":"Ganesh","sequence":"additional","affiliation":[{"name":"CNRS (Centre National de la Recherche Scientifique), Universite Montpellier (UM) Laboratoire de Informatique de Robotique et de Microelectronique de Montpellier (LIRMM), Montpellier, France"},{"name":"CNRS-AIST JRL (Joint Robotics Laboratory), IRL3218, National Institute of Advanced Industrial Science and Technology, Tsukuba Central 1, 1-1-1 Umezono, Tsukuba 305-8560, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hideyuki","family":"Ando","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Technology, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yasuharu","family":"Koike","sequence":"additional","affiliation":[{"name":"Institute of Innovative Research, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eiichi","family":"Yoshida","sequence":"additional","affiliation":[{"name":"CNRS-AIST JRL (Joint Robotics Laboratory), IRL3218, National Institute of Advanced Industrial Science and Technology, Tsukuba Central 1, 1-1-1 Umezono, Tsukuba 305-8560, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Natsue","family":"Yoshimura","sequence":"additional","affiliation":[{"name":"Institute of Innovative Research, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2021,8,11]]},"reference":[{"key":"S0129065721500349BIB001","first-page":"2249","volume-title":"Proc. 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