QQ客服 官方微博 反馈留言

	Adaptive feature extraction for EEG signal classification
	Sun, Shiliang ; Zhang, Changshui
	2010-05-06 ; 2010-05-06 ; OCT
关键词	brain-computer interface (BCI) common spatial patterns (CSP) EEG signal classification feature extraction BRAIN-COMPUTER INTERFACE Computer Science, Interdisciplinary Applications Engineering, Biomedical Mathematical & Computational Biology Medical Informatics
中文摘要	One challenge in the current research of brain-computer interfaces (BCIs) is how to classify time-varying electroencephalographic (EEG) signals as accurately as possible. In this paper, we address this problem from the aspect of updating feature extractors and propose an adaptive feature extractor, namely adaptive common spatial patterns (ACSP). Through the weighed update of signal covariances, the most discriminative features related to the current brain states are extracted by the method of multi-class common spatial patterns (CSP). Pseudo-online simulations of EEG signal classification with a support vector machine (SVM) classifier for multi-class mental imagery tasks show the effectiveness of the proposed adaptive feature extractor.
语种	英语 ; 英语
出版者	SPRINGER HEIDELBERG ; HEIDELBERG ; TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/9211]
专题	清华大学
推荐引用方式 GB/T 7714	Sun, Shiliang,Zhang, Changshui. Adaptive feature extraction for EEG signal classification[J],2010, 2010, OCT.
APA	Sun, Shiliang,&Zhang, Changshui.(2010).Adaptive feature extraction for EEG signal classification..
MLA	Sun, Shiliang,et al."Adaptive feature extraction for EEG signal classification".(2010).

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。