Abstract
It has been shown previously that recognizing persons using 40 Hz electroencephalogram (EEG) oscillations is possible. In the method, features were computed from the visual evoked potential (VEP) signals recorded from 61 electrodes while subjects perceived a picture. Here, two modifications have been proposed to improve the classification performance: principal component analysis (PCA) to reduce the noise and background EEG effects from the VEP signals and normalization. Two classifiers were used: simplified fuzzy ARTMAP (SFA), 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 and normalization improved the classification performance for both the classifiers. The best classification performance of 95.25% obtained using the improved method shows that 40 Hz EEG oscillations are suitable for use as biometrics
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