Power Spectral Density Analysis for Human EEG-based Biometric Identification

Abstract

Authentication is most important for security. There are many different systems for recognizing the person. The traditional authentication systems such as passwords have drawbacks. It is easy to be stolen. Biometric authentication systems provide the best security. However, a current technique that widely used for identification which is fingerprint has its own disadvantages. Furthermore, current techniques such as facial recognition, iris recognition and voice recognition that used to recognize person still compromise the security walls. In this recent years, electroencephalograph (EEG) signal has been discovered that it has the potential to become one of the biometric authentication systems. It is brain activities for a human. It is unique due to the EEG signal is different from person to person. In this paper, power spectral density analysis was used to analyse the electroencephalography (EEG) signal. K-nearest neighbor classifier was used for classification in this paper. The accuracy results of alpha (8–13 Hz), beta (13–30 Hz), combined alpha and beta (8–30 Hz) and combined theta, alpha, beta and gamma (4–40 Hz) frequency bands were compared. Overall, the percentage of accuracy was above 80%. The most suitable frequency bands for human EEG-based biometric identification in this experiment was the combined theta, alpha, beta, and gamma (4–40 Hz). The percentage of accuracy for this frequency band was the highest among the others which is 89.21%.