An Independent Component Analysis (ICA) Based Approach for EEG Person Authentication

摘要：

Exploring brain electrical activity represented by electroencephalogram (EEG) signals for biometric applications has recently attracted increasing research attention since EEG pattern has been shown to be unique for each individual. In this paper, we propose an Independent Component Analysis (ICA) based EEG feature extraction and modeling approach for person authentication. Five dominating Independent Components (DIC) are determined from five brain regions represented by EEG channels, then univariate autoregressive coefficients of DICs are extract as features. Based on AR coefficients of DICs, a Naive Bayes probabilistic model is employed for person authentication purpose. Results from a real EEG motor task study suggest that the proposed ICA-based approach is promising and may open new directions in the emerging EEG biometry area.

作者: Chen He Z.Jane Wang

作者单位: Department of Electrical and Computer Engineering University of British Columbia Vancouver,British Columbia,Canada

会议类型: 国际会议

会议名称: The 3rd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议)

会议地点: 北京

会议语种:英文

页码: 1-4

在线出版日期: 2009-06-11（万方平台首次上网日期，不代表论文的发表时间）