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{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,3]],"date-time":"2024-08-03T21:10:35Z","timestamp":1722719435469},"reference-count":29,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T00:00:00Z","timestamp":1660262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Kumoh National Institute of Technology"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"A novel whitening technique for motor imagery (MI) classification is proposed to reduce the accuracy variance of brain\u2013computer interfaces (BCIs). This method is intended to improve the electroencephalogram eigenface analysis performance for the MI classification of BCIs. In BCI classification, the variance of the accuracy among subjects is sensitive to the accuracy itself for superior classification results. Hence, with the help of Gram\u2013Schmidt orthogonalization, we propose a BCI channel whitening (BCICW) scheme to minimize the variance among subjects. The newly proposed BCICW method improved the variance of the MI classification in real data. To validate and verify the proposed scheme, we performed an experiment on the BCI competition 3 dataset IIIa (D3D3a) and the BCI competition 4 dataset IIa (D4D2a) using the MATLAB simulation tool. The variance data when using the proposed BCICW method based on Gram\u2013Schmidt orthogonalization was much lower (11.21) than that when using the EFA method (58.33) for D3D3a and decreased from (17.48) to (9.38) for D4D2a. Therefore, the proposed method could be effective for MI classification of BCI applications.<\/jats:p>","DOI":"10.3390\/s22166042","type":"journal-article","created":{"date-parts":[[2022,8,16]],"date-time":"2022-08-16T03:44:03Z","timestamp":1660621443000},"page":"6042","source":"Crossref","is-referenced-by-count":5,"title":["Whitening Technique Based on Gram\u2013Schmidt Orthogonalization for Motor Imagery Classification of Brain\u2013Computer Interface Applications"],"prefix":"10.3390","volume":"22","author":[{"given":"Hojong","family":"Choi","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Gachon University, Seongnam 13306, Korea"}]},{"given":"Junghun","family":"Park","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-2688-302X","authenticated-orcid":false,"given":"Yeon-Mo","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,12]]},"reference":[{"key":"ref_1","unstructured":"Jin, J., Wang, Z., Xu, R., Liu, C., Wang, X., and Cichocki, A. 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