Research on real-time security authentication method based on EEG data features

Electroencephalographic (EEG) signals have attracted much attention as a desirable new type of biometric traits due to their unique advantages of security, concealment and evasion. In this paper, we used the EMOTIV EPOC+ head-mounted EEG device to collect EEG signals from participants through 14 electrode channels, including AF3 and AF4. The processing methods of peak-to-peak amplitude detection and iterative averaging denoising are proposed for the presence of ocular electrical interference and industrial frequency interference in the signal, respectively. The processed signal data is fed into the authentication model of the deep convolutional recurrent neural network constructed in this paper to carry out security authentication test experiments. The average authentication accuracy of this authentication model can reach 92.60%, which improves the accuracy compared to the LDA classifier. In the “self-stranger” and “selfacquaintance” categorization tests, the accuracy of this paper’s method curve is always higher than that of random selection and outperforms PCA on some feature bands. The deep learning model of CNN fused with LSTM can make full use of EEG data features to defend against illegal users in real time.