标题:Semi-supervised regularized discriminant analysis for EEG-based BCI system
作者:Xin, Yuhang ;Wu, Qiang ;Zhao, Qibin ;Wu, Qi
通讯作者:Wu, Qiang
作者机构:[Xin, Yuhang ;Wu, Qiang ] School of Information Science and Engineering, Shandong University, Jinan; Shandong; 250010, China;[Wu, Qi ] Qilu Hospital, 更多
会议名称:18th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2017
会议日期:30 October 2017 through 1 November 2017
来源:Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
出版年:2017
卷:10585 LNCS
页码:516-523
DOI:10.1007/978-3-319-68935-7_56
摘要:Brain-Computer interface (BCI) is a new technique which allows direction connection between human and computer or other external device. It employs the classification of event-related potential to control the equipment rather than using language or limb movement. Currently, one of the most important issues for ERP-based BCIs is that the ERP classification performance degrades when the training number of samples is small. In order to solve this problem, semi-supervised regularized discriminant analysis (SRDA) was proposed to extract features and classify ERP patterns by integrating semi-supervised learning and regularization approach. The labeled data was used to maximize the separability between different classes and calculate the within-class covariance matrix by regularization. The labeled and unlabeled data were employed to construct the penalty term by neighbor graph. Our proposed approach was evaluated on the BCI Competition Challenge Dataset and the simulation results indicated that it achieved a better accuracy than the traditional algorithms. © Springer International Publishing AG 2017.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034254855&doi=10.1007%2f978-3-319-68935-7_56&partnerID=40&md5=4c3bd6c38948e9af1b6a3a9860268612
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