The Study Of EEG-Based Personal Authorization

Authorizing personal identity in a safe way is important in the social life nowadays. In recent years, there are lots of methods to authorize identity. Among them, the method based on biometric information has become one of the most popular ways because of its security and convenience. There have been more and more researches in personal authentication based on fingerprint, iris, voice and gait. Compared with face, fingerprint, voice, iris, signature or other widely used biometrics, EEG has two distinct advantages. First, the active Electroencephalogram (EEG) must come from a living individual with a normal mental state, and an aggressor cannot force the person to provide the ideal EEG signals as those recorded in normal states. Second, EEG is hard to mimic. Therefore, EEG can be an excellent complement modality to the existing biometric systems which are prone to forgery.The paper focuses on the research in personal identification with the single channel EEG signal. An improved algorithm is propose that can not only find who he is if the subject belongs to the data set (closed-set identification) but also can reject the subject if he/she is out of the data set (rejection). Besides, metric learning is used in our algorithm to reduce the required EEG signal’s recording duration, and keep almost the same auucracy.tThe main contributions are as follows:1. The auto-regression (AR) model is improved, and feature level fusion is proposed including the power spectral density, AR model, and wavelet feature. Furthermore, the linear discriminant analysis (LDA) is used to reduce the dimensions of the cascaded feature. Experimental results shows the effectiveness of the proposed method.2. Metric learning is introduced to reduce the required EEG signal’s recording duration, and keep almost the same accuracy.3. A two-level classifier with naive Bayes classifier and K nearest neighborhood (KNN) is proposed, which can not only identify the legal person in the data set, but also reject the illegal person out of the data set. Finally, we achieved that the false positive rate is6%while the false negative rate is5%, and the final open=set identification accuracy rate is88.7%.