Investigation of Channel Reduction Based on Brain Lobes in EEG-based Authentication System

Biometric authentication is recently used for verification someone’s identity according to their physiological and behavioural characteristics. The most popular biometric techniques are fingerprints, facial and voices recognition. However, these techniques have the disadvantage in which they can easily be imitated and mimicked by hackers to access a device or a system. Therefore, this study proposed electroencephalogram (EEG) as a biometric technique to encounter this problem. The wavelet packet decomposition is explored in this study for the feature extraction method. The wavelet packet decomposition feature is represented, root mean squared (RMS) wavelet features to extract a piece of meaningful information from the original EEG signal. These features were applied to classify between 15 subjects by using Support Vector Machine (SVM). The channel reduction was conducted to investigate the brain lobe effectiveness during the paradigms of familiar and unfamiliar EEG signals which the channel reduction is based on the brain lobes (temporal, occipital, parietal, and frontal). As a result, the above 14 channels obtained the best performance of the system which is 97.44% of correct recognition rate (CRR). The analysis of the paradigms among familiar only, unfamiliar only, and both familiar and unfamiliar was conducted to evaluate the contribution of the paradigms. The results show that 14 channels obtained the best familiar paradigms while the other contributed by unfamiliar. The result is promising because the CRR computed above 90%, however further analysis of channel reduction has to be work to obtain specific channel to develop the small number of channel for comfort and convenience biometric sensor which is suitable for future authentication.