Method of Individual Identification Based on Electroencephalogram Analysis

Abstract

Biometric based on Electroencephalogram have proved to be unique enough between subjects for applications. A new method on identifying the individuality of persons by using parametric was used for identification of motor imagery. In this paper, autoregressive mode, phase synchronization, Energy Spectral Density and linear complexity value were used as EEG features. Neural network was employed for identification of individual differences. Then, identification rate was analyzed by different data length and wave band. The result shows that high identification ratio was tongue movement and that perfect accuracy depends on the Paradigm of motor imagery and wave band.