Abstract
As a biometric characteristic, electroencephalography (EEG) signals have the advantages of being hard to steal and easy to detect liveness, which attract researchers to study EEG-based personal identification technique. Among different EEG protocols, resting state signals are the most practical option since it is more convenient to operate than the other protocols. In this paper, a personal identification system based on resting state EEG is proposed, in which data augmentation and convolutional neural network are combined. The cross-validation is performed on a public database of 109 subjects. The experimental results show that when only 14 EEG channels and 0.5 seconds data are employed, the average accuracy and average equal error rate of the system can reach 99.32% and 0.18%, respectively. Compared with some existing representative works, the proposed system has the advantages of short acquisition time, low computational complexity, and rapid deployment using market available low-cost EEG sensors, which further advances the implementation of practical EEG-based identification systems.
Highlights
As society enters digital era, identification has become vital in people’s work and life
Liveness detection is not easy to achieve for these biometrics. e brain signal represented by electroencephalography (EEG) based biometric technique may solve such problems and has become a prominent personal identification method [7]
When sliding windows with an overlap rate of 50% were used, the corresponding Rank-1 accuracy was 99.40%, 99.51%, 99.04%, and 92.74%, respectively, and the equal error rates were improved to 0.15%, 0.06%, 0.19%, and 1.67%. e results show that sliding windows achieved a better performance than fixed windows
Summary
As society enters digital era, identification has become vital in people’s work and life Traditional identification technologies, such as password and hardware token, may be forgotten, lost, or stolen, resulting in identity leakage or identification failure [1]. Such problems can be avoided by using biometric identification techniques, such as face, fingerprints, and gait, which have been widely studied [2,3,4]. E brain signal represented by electroencephalography (EEG) based biometric technique may solve such problems and has become a prominent personal identification method [7]. Low-cost sensor systems help to increase the size of subjects and avoid system performance failures caused by a lack of sample diversity [12]
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