{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T04:57:56Z","timestamp":1781585876575,"version":"3.54.5"},"reference-count":35,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T00:00:00Z","timestamp":1709596800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chinese Academy of Medical Science health innovation project","award":["2021-I2M-1-042"],"award-info":[{"award-number":["2021-I2M-1-042"]}]},{"name":"Chinese Academy of Medical Science health innovation project","award":["2022-I2M-C&T-A-005"],"award-info":[{"award-number":["2022-I2M-C&T-A-005"]}]},{"name":"Chinese Academy of Medical Science health innovation project","award":["2022-I2M-C&T-B-012"],"award-info":[{"award-number":["2022-I2M-C&T-B-012"]}]},{"name":"Chinese Academy of Medical Science health innovation project","award":["20JCJQIC00230"],"award-info":[{"award-number":["20JCJQIC00230"]}]},{"name":"Tianjin Outstanding Youth Fund Project","award":["2021-I2M-1-042"],"award-info":[{"award-number":["2021-I2M-1-042"]}]},{"name":"Tianjin Outstanding Youth Fund Project","award":["2022-I2M-C&T-A-005"],"award-info":[{"award-number":["2022-I2M-C&T-A-005"]}]},{"name":"Tianjin Outstanding Youth Fund Project","award":["2022-I2M-C&T-B-012"],"award-info":[{"award-number":["2022-I2M-C&T-B-012"]}]},{"name":"Tianjin Outstanding Youth Fund Project","award":["20JCJQIC00230"],"award-info":[{"award-number":["20JCJQIC00230"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Motor imagery (MI)-based brain\u2013computer interface (BCI) has emerged as a crucial method for rehabilitating stroke patients. However, the variability in the time\u2013frequency distribution of MI-electroencephalography (EEG) among individuals limits the generalizability of algorithms that rely on non-customized time\u2013frequency segments. In this study, we propose a novel method for optimizing time\u2013frequency segments of MI-EEG using the sparrow search algorithm (SSA). Additionally, we apply a correlation-based channel selection (CCS) method that considers the correlation coefficient of features between each pair of EEG channels. Subsequently, we utilize a regularized common spatial pattern method to extract effective features. Finally, a support vector machine is employed for signal classification. The results on three BCI datasets confirmed that our algorithm achieved better accuracy (99.11% vs. 94.00% for BCI Competition III Dataset IIIa, 87.70% vs. 81.10% for Chinese Academy of Medical Sciences dataset, and 87.94% vs. 81.97% for BCI Competition IV Dataset 1) compared to algorithms with non-customized time\u2013frequency segments. Our proposed algorithm enables adaptive optimization of EEG time\u2013frequency segments, which is crucial for the development of clinically effective motor rehabilitation.<\/jats:p>","DOI":"10.3390\/s24051678","type":"journal-article","created":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T05:31:19Z","timestamp":1709616679000},"page":"1678","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Adaptive Time\u2013Frequency Segment Optimization for Motor Imagery Classification"],"prefix":"10.3390","volume":"24","author":[{"given":"Junjie","family":"Huang","sequence":"first","affiliation":[{"name":"China Academy of Information and Communications Technology, Beijing 100191, China"},{"name":"Key Laboratory of Internet and Industrial Integration Innovation, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9526-3860","authenticated-orcid":false,"given":"Guorui","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300192, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Academy of Information and Communications Technology, Beijing 100191, China"},{"name":"Key Laboratory of Internet and Industrial Integration Innovation, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingmin","family":"Yu","sequence":"additional","affiliation":[{"name":"China Academy of Information and Communications Technology, Beijing 100191, China"},{"name":"Key Laboratory of Internet and Industrial Integration Innovation, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300192, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1016\/S1388-2457(02)00057-3","article-title":"Brain-computer interfaces for communication and control","volume":"113","author":"Wolpaw","year":"2002","journal-title":"Suppl. 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