Using Convolutional Neural Networks for Identification Based on EEG Signals

Abstract

Biometrics identification uses human biometric such as human faces, fingerprints, irises for authentication. Electroencephalograph (EEG) provides a detectable neuroelectrophysiological signal that could reflect psychological or behavioral characteristics. In the past, EEG -based biometrics were usually used with machine learning based procedure for identification. Recently Convolutional Neural Networks (CNN) was used as a new tool for biometric automatic feature exaction and classification. This paper aims at investigating CNN's performance on EEG -based human identification with the increase number of subjects. P300 -speller was introduced in this study to induce Event -related potential (ERP). 33 healthy subjects participated in the experiment. Result shows that the CNN -based Biometric System reached a high degree of accuracy (99.9%) after for 8 -class classification , 99.3% for 10 -class and 99.3% for 13 -class. Then, we made some finetuning to the network structure of the 10 -class and the 13 class classification, Then,we also improved the convergence speed of the network and reduced the time to get the best classification model by changing the structure of the CNN that we introduced here, while ensuring that the accuracy and the loss function did not change significantly. The finding indicts that CNN get good performance in EEG -based biometric identification.