Personal Identification Based on Content-Independent EEG Signal Analysis

Abstract

Interests in the use of biological signals have been rapidly growing in the past decades. Biometrics recognition based on electroencephalogram (EEG) has become a hotspot. In this paper, we propose a novel EEG biometrics system. The system contains automatic channel selection, wavelet feature extraction and Deep Neural Network (DNN) classifier. The channel selection can not only reduce the computational redundancy, but also improve the accuracy. A strategy of fusing EEG and physiological signal is adopted in the system. As a very useful supplement to other previous work, we specially endeavor to handle content-independent EEG biometrics. The proposed system is validated on a multimodal dataset, i.e. DEAP [1] for the authentication of the identity. We perform data augmentation through splitting the EEG signal by down sampling with different shift. An accuracy of 94%, ±, 3% is obtained in 10-fold validations. The results demonstrate the possibility of EEG biometrics under content-independent scenario.