Affiliations: [a] School of Information & Communications Technology, Republic Polytechnic, 738964, Singapore | [b] Faculty of Information Science and Technology, Multimedia University, Melaka, 75450, Malaysia | [c] Department of Computer Science, University of Essex, Colchester, CO4 3SQ, United Kingdom
Abstract: In previous studies, identification of individuals using 61 channel Visual Evoked Potential (VEP) signals from the brain has been shown to be feasible. These studies used neural network classification of gamma band spectral power of VEP signals from 20 individuals. This paper explores our continuing work in this area to include more subjects in the experiment and to reduce the number of required channels using Fisher Discriminant Ratio function. The experimental study showed that 27 optimal channels were sufficient to yield an average classification rate of 90.97% across 800 test VEP patterns from 40 subjects. Being fewer in number than 61 channels, it is less cumbersome, requires lower computational time, design complexity and cost. This was achieved without loss of performance as 61 channels gave an average classification result of 89.11% The positive results obtained here showed that the neural activity during perception of visual stimulus was different across individuals. This method could be explored further as a biometric tool to identify individuals as the brain signals are difficult to be forged.
