{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T07:56:42Z","timestamp":1764403002476,"version":"3.37.3"},"reference-count":24,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,6,30]],"date-time":"2020-06-30T00:00:00Z","timestamp":1593475200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61673316","2015B010104002","17A148","2019S019"],"award-info":[{"award-number":["61673316","2015B010104002","17A148","2019S019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Major Science & Technology Project of Guangdong Province","award":["61673316","2015B010104002","17A148","2019S019"],"award-info":[{"award-number":["61673316","2015B010104002","17A148","2019S019"]}]},{"DOI":"10.13039\/100009377","name":"Education Department of Hunan Province","doi-asserted-by":"publisher","award":["61673316","2015B010104002","17A148","2019S019"],"award-info":[{"award-number":["61673316","2015B010104002","17A148","2019S019"]}],"id":[{"id":"10.13039\/100009377","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Planning Projects of Changde City","award":["61673316","2015B010104002","17A148","2019S019"],"award-info":[{"award-number":["61673316","2015B010104002","17A148","2019S019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2020,6,30]]},"abstract":"<jats:p>In order to improve the accuracy of brain signal processing and accelerate speed meanwhile, we present an optimal and intelligent method for large dataset classification application in this paper. Optimized Extreme Learning Machine (OELM) is introduced in ElectroCorticoGram (ECoG) feature classification of motor imaginary-based brain-computer interface (BCI) system, with common spatial pattern (CSP) to extract the feature. When comparing it with other conventional classification methods like SVM and ELM, we exploit several metrics to evaluate the performance of all the adopted methods objectively. The accuracy of the proposed BCI system approaches approximately 92.31% when classifying ECoG epochs into left pinky or tongue movement, while the highest accuracy obtained by other methods is no more than 81%, which substantiates that OELM is more efficient than SVM, ELM, etc. Moreover, the simulation results also demonstrate that OELM will significantly improve the performance with <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mi>p<\/mml:mi><\/mml:math> value being far less than 0.001. Hence, the proposed OELM is satisfactory in addressing ECoG signal.<\/jats:p>","DOI":"10.1155\/2020\/2913019","type":"journal-article","created":{"date-parts":[[2020,6,30]],"date-time":"2020-06-30T23:41:37Z","timestamp":1593560497000},"page":"1-13","source":"Crossref","is-referenced-by-count":6,"title":["An ECoG-Based Binary Classification of BCI Using Optimized Extreme Learning Machine"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4381-7796","authenticated-orcid":true,"given":"Xinman","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, MOE Key Lab for Intelligent Networks and Network Security, Xi\u2019an Jiaotong University, Xi\u2019an, Shaanxi 710049, 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