title: High-accuracy user identification using EEG biometrics.
abstract: We analyze brain waves acquired through a consumer-grade EEG device to investigate its capabilities for user identification and authentication. First, we show the statistical significance of the P300 component in event-related potential (ERP) data from 14-channel EEGs across 25 subjects. We then apply a variety of machine learning techniques, comparing the user identification performance of various different combinations of a dimensionality reduction technique followed by a classification algorithm. Experimental results show that an identification accuracy of 72% can be achieved using only a single 800 ms ERP epoch. In addition, we demonstrate that the user identification accuracy can be significantly improved to more than 96.7% by joint classification of multiple epochs.
authors.forename: Toshiaki
authors.initials: T
authors.lastname: Koike-Akino
authors.forename: Ruhi
authors.initials: R
authors.lastname: Mahajan
authors.forename: Tim K
authors.initials: TK
authors.lastname: Marks
authors.lastname: Ye Wang
authors.forename: Shinji
authors.initials: S
authors.lastname: Watanabe
authors.forename: Oncel
authors.initials: O
authors.lastname: Tuzel
authors.forename: Philip
authors.initials: P
authors.lastname: Orlik
mesh_terms: D000465:Algorithms; D056667:Biometric Identification / Q000379:methods*; D001921:Brain / Q000502:physiology; D004569:Electroencephalography / Q000295:instrumentation / Q000379:methods*; D004867:Equipment Design; D018913:Event-Related Potentials, P300 / Q000502:physiology; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D016015:Logistic Models; D000069550:Machine Learning; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted