Fisher linear discriminant based person identification using visual evoked potentials

Biometrics is the technique of uniquely recognizing a person among a group of people. It is usually performed based on one or more of human's intrinsic physical or behavioral traits. One such trait is the electroencephalogram (EEG) signal. In this paper, the feasibility of Visual Evoked Potential (VEP) in the gamma band of EEG signal, as a physiological trait, is studied, and used to identify individuals in a group of 20 people. To this end, the parameters of the AR model together with the peak of the power spectrum density (PSD) of the gamma band VEP signal (GMVEP) are considered as main useful features. Next, the Fisher's Linear Discriminant (FLD) is used to reduce the feature vector dimensions. Finally, the K Nearest Neighborhood (KNN) technique is employed to classify the data and the leave-one-out cross validation method is used for accuracy assessment. A correct classification rate of 100% is achieved.