Abstract
As a common bioelectric phenomenon, brainwave is produced by electric field change in human brain. The use of electroencephalogram (EEG) in identity recognition greatly enhances recognition security and accuracy. In this work, we propose a secure identity recognition method by using EEG with multi-factor feature. Machine learning is utilized to extract, classify and select features of EEG data. Structural sparse norm in EEG data has consistency. The feature is helpful in optimization. The experiments show that SIRSE has superiority on security, recognition success rate and reliability in comparison.
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