Analysis of effective channel placement for an EEG-based biometric system

This paper discusses the potential of the EEG signal for implementation of a practical biometric system using 4 or less channels of 2 different types of EEG recordings. Studies have shown that the EEG signal has biometric potential because the signal varies from person to person and is impossible to replicate and steal. Data were collected from 10 male subjects while resting with eyes open and eyes closed in 5 separate sessions conducted over a course of 2 weeks. Features were extracted using the autoregressive (AR) model and analyzed to obtain the feature set. Results show that data from eyes open and eyes closed using 4 channels gave good classification rates of 96% and 97% respectively and that data recorded from 2 channels gave classification rates from 90% to 95%. Classification rates from 1 channel ranged from 70% to 87%. The average time taken for recognition was 0.38 seconds at the point of recognition. Based on these results, there is potential for implementation of an EEG-based biometric system.