Skip to Main Content
A method of identifying individuals using visual-evoked-potential (VEP) signals and a neural network (NN) is proposed. In the approach, a backpropagation (BP) NN is trained to identify individuals using the gamma-band (30-50 Hz) spectral power ratio of VEP signals extracted from 61 electrodes located on the scalp of the brain. The gamma-band spectral-power ratio is computed using a zero-phase Butterworth digital filter and Parseval's time-frequency equivalence theorem. NN classification gives an average of 99.06% across 400 test VEP patterns from 20 individuals using a 10-fold cross-validation scheme. This shows promise for the approach to be developed further as a biometric identification system.