Individual Feature Extraction and Identification on EEG Signals in Relax and Visual Evoked Tasks

Abstract

Compared to conventional biometrics, electroencephalogram (EEG) signal has obvious advantages in uniqueness, high confidentiality and impossibility to steal or mimic. In this paper, we investigated EEG signals in relax task and visual evoked task and compared their potentials as the biometric authentication feature. 20 subjects were recruited, and each performed two tasks while 64-channel EEG signals were recorded continuously. The extracted features, autoregression (AR) model, power spectrum of the time-domain (TPS), power spectrum of the frequency-domain (FPS) and phase-locking value (PLV), were given to a support vector machine (SVM) for classification respectively. The results showed that visual evoked task presented better performance in identifying the individuals than the relax task did. Specially, among all these features, AR model got the highest accuracy in both tasks, achieving 90.53% and 96.25% respectively for relax task and visual evoked task. Then support vector machine-recursive feature elimination (SVM-RFE) was employed to select the most discriminative channels just for AR model based on VEP signals for it showed the best performance. Additionally, it gave a higher accuracy of 97.25% based on the 32 top ranked channels. Further investigation may help develop an alternative EEG based biometric system to enhance the traditional biometric technologies. © Springer-Verlag Berlin Heidelberg 2014.