Improving visual evoked potential feature classification for person recognition using PCA and normalization

In earlier papers, it was shown that recognizing persons using their brain patterns evoked during visual stimulus is possible. In this paper, several modifications are proposed to improve the recognition accuracy. In the method, gamma band spectral power (GBSP) features were computed from the visual evoked potential (VEP) signals recorded from 61 electrodes while subjects perceived a picture. Two methods were used to improve the classification rate. First, principal component analysis (PCA) was used to reduce the noise and background electroencephalogram (EEG) effects from the VEP signals. Second, the GBSP of each channel was normalized by the total GBSP from all the channels. Three classifiers were used: simplified fuzzy ARTMAP (SFA), linear discriminant (LD) and k-nearest neighbor (kNN). The experimental results using 800 VEP signals from 20 subjects with leave-one-out cross-validation strategy showed that PCA improves the classification performance for all the classifiers with normalization giving improved results in certain cases. The best classification performance of 96.50% obtained using the improved method shows that brain signals have suitable biometric properties that could be further exploited.