Person Authentication System Using Feature Level Fusion of a Single Channel EEG Signal

With the advent of biometric knowledge, conventional methods of authentication are being replaced with biometric based methods. Recently, the use of EEG signal in biometric systems attracted increasing research attention. Only a few works have been done in this emerging of EEG-based biometry mainly focusing on person identification not on person authentication. This paper examines the effectiveness of the EEG as a biometric for person authentication. In this study, the EEG signal from fifteen volunteer recorded during imagination of opening and closing fist was used. A set of AR coefficients, power of spectral bands, Energy Spectral Density, Energy Entropy and Sample Entropy were used as extracted features. The authentication system is fused at the sensor module and features to support a system which can meet more challenging and varying requirements. The utility of the sequential search methods is also experimentally studied. In the extensive experimentation on the Shalk and his colleague’s database, we demonstrate that with combination of features when using single channel EEG, the performance of system is improved in two ways of single block and multi block methods compared to other. Result of this study shows a clear vision of commercial and practical use of the brain's electrical signals in the authentication systems of future.