Network weight adjustment in a fractional fourier transform based multi-channel brain computer interface for person authentication

Brain is composed of unique complex neural structure thus electrical activity between neurons referred to as electroencephalogram (EEG) in different brain regions varies from one user to another. In this paper EEG distinctiveness is exploited through application to person authentication system based on five mental imagery tasks. Seven electrodes placed at C3, C4, P3, P4, O1, O2 and EOG are used to record EEG signals. A parallel structure of Exact Radial Basis (RBE) neural networks are used as classifiers. Individual classifier response for each mental task is evaluated and a weighting approach is used to regulate contribution of each channel within a multi-channel Brain Computer Interface (BCI) system. The estimated and experimental results indicate an average increase of 14% in system performance when tested on 722 trials of 1sec duration for 7 subjects. Fractional Fourier Transform (FRFT) with order optimization is used for feature extraction, and special one dimensional case of k-means clustering algorithm is used to calculate the threshold for individual classifiers.