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DOI码：10.1109/EMBC.2015.7320064

发表刊物：2015 37TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)

刊物所在地：UNITED STATES

关键字：FAMILIARITY

摘要：Research on brain machine interface (BMI) has been developed very fast in recent years. Numerous feature extraction methods have successfully been applied to electroencephalogram (EEG) classification in various experiments. However, little effort has been spent on EEG based BMI systems regarding familiarity of human faces cognition. In this work, we have implemented and compared the classification performances of four common feature extraction methods, namely, common spatial pattern, principal component analysis, wavelet transform and interval features. High resolution EEG signals were collected from fifteen healthy subjects stimulated by equal number of familiar and novel faces. Principal component analysis outperforms other methods with average classification accuracy reaching 94.2% leading to possible real life applications. Our findings thereby may contribute to the BMI systems for face recognition.

合写作者：Sen Ma, Zhongze hu,陈健生, Guangda Su,Weitun Yang, Xiaojie Zhang, Min Lu, Weibei Dou

第一作者：Yudu Li,Minli Liao

论文类型：会议论文

通讯作者：陈健生,Minli Liao,Minli Liao

页面范围：7246-7249

ISSN号：1557-170X

是否译文：否

发表时间：2015-01-01