Selecting Relevant EEG Signal Locations for Personal Identification Problem Using ICA and Neural Network

The problem of identifying a person using biometric data may be of interest. In this paper, EEG signals are used to identify a person as different persons have different EEG patterns. EEG signals can be measured from different locations. Too many signals can degrade the recognition speed and accuracy. A practical technique combining independent component analysis (ICA) for signal cleaning and a supervised neural network for classifying signals is proposed. From 16 EEG different signal locations, three truly relevant locations F_P1, P₃, and C₄ were selected. This selection is based on signals obtained from the subjects at the Comprehensive Epilepsy Program of Chulalongkorn University Hospital, Bangkok, Thailand.