Task-Independent EEG based Subject Identification using Auditory Stimulus

Abstract

Recent studies have shown that task-specific electroencephalography (EEG) can be used as a reliable biometric. This paper extends this study to task-independent EEG with auditory stimuli. Data collected from 40 subjects in response to various types of audio stimuli, using a 128 channel EEG system is presented to different classifiers, namely, k-nearest neighbor (k-NN), arti-ficial neural network (ANN) and universal background model - Gaussian mixture model (UBM-GMM). It is observed that k-NN and ANN perform well when testing is performed intrasession, while UBM-GMM framework is more robust when testing is performed intersession. This can be attributed to the fact that the correspondence of the sensor locations across sessions is only approximate. It is also observed that EEG from parietal and temporal regions contain more subject information although the performance using all the 128 channel data is marginally better.