multifeature biometric system based on eeg signals
This paper focuses on the person authentication problem based on EEG signals. Many features extracted from EEG recordings have proved to be unique enough among subjects for biometric application. However, different features show different discriminative power for different subjects and different trials. We present here an authentication system, which make use of a multifeature fusion architecture. The results demonstrate to improve the system performance. Depending on the security level, different thresholds can be applied. The influence of different thresholds on system performance is discussed.
multifeature biometric authentication electroencephalogram (eeg) motor imagery