High-accuracy user identification using EEG biometrics.
2016 38TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)(2016)
摘要
We analyze brain waves acquired through a consumer-grade EEG device to investigate its capabilities for user identification and authentication. First, we show the statistical significance of the P300 component in event-related potential (ERP) data from 14-channel EEGs across 25 subjects. We then apply a variety of machine learning techniques, comparing the user identification performance of various different combinations of a dimensionality reduction technique followed by a classification algorithm. Experimental results show that an identification accuracy of 72% can be achieved using only a single 800 ms ERP epoch. In addition, we demonstrate that the user identification accuracy can be significantly improved to more than 96.7% by joint classification of multiple epochs.
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关键词
Algorithms,Biometric Identification,Brain,Electroencephalography,Equipment Design,Event-Related Potentials, P300,Evoked Potentials,Humans,Logistic Models,Machine Learning,Neural Networks (Computer),Signal Processing, Computer-Assisted
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