Towards EEG biometrics: pattern matching approaches for user identification

Abstract

EEG brainwaves have recently emerged as a promising biometric that can be used for individual identification, since those signals are confidential, sensitive, and hard to steal and replicate. In this study, we propose a new stimulidriven, non-volitional brain responses based framework towards individual identification. The non-volitional mechanism provides an even more secure way in which the subjects are not aware of and thus can not manipulate their brain activities. We present our preliminary investigations based on two pattern matching approaches: Euclidean Distance (ED) and Dynamic Time Warping (DTW). We investigate the performance of our proposed methods using four different visual stimuli and the potential impacts from four different EEG electrode channels. Experimental results show that, the Oz channel provides the best identification accuracy for both ED and DTW methods, and the stimuli of illegal strings and words seem to trigger more distinguishable brain responses. For ED method, the accuracy of identifying 30 subjects could reach over 80%, which is better than the best accuracy of about 68% that can be achieved by DTW method. Our study lays a foundation for future investigation of brainwave-based biometric approaches.