NDLI: Single trial EEG classification applied to a face recognition experiment using different feature extraction methods

Content Provider	IEEE Xplore Digital Library
Author	Yudu Li Sen Ma Zhongze Hu Jiansheng Chen Guangda Su Weibei Dou
Copyright Year	2015
Description	Author affiliation: Dept. of Electron. Eng., Tsinghua Univ., Beijing, China (Yudu Li; Sen Ma; Zhongze Hu; Jiansheng Chen; Guangda Su; Weibei Dou)
Abstract	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.
Starting Page	7246
Ending Page	7249
File Size	689644
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7320064
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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