Biometrics Based on Single-Trial EEG

Abstract

The biometrics based on resting state electroencephalography (EEG) is better than other EEG-based authentication protocols in terms of usability because it does not require any external stimuli and has a relatively short authentication time. Most of previous resting state EEG-based authentication systems have used a relatively long EEG data (e.g., > 1 min) measured once, and they were segmented to create many trials (e.g., > 100). In this case, however, it is difficult to reflect real-authentication situations in which a user repetitively uses an authentication system in different time points. Therefore, we propose to use single trials repetitively measured for short time (10 s). In the experiment, resting state EEGs were measured while fifteen subjects opened and closed their eyes 30 times for 10 s each. The measured EEG data were divided into three conditions, which are eyes open (EO), eyes closed (EC), and difference between EC and EO (Diff). We extracted power spectral density (PSD) ranging from 3 to 20 Hz as features for classification, with which a binary classification based on a 5×5-fold cross-validation was performed for each subject using linear discriminant analysis (LDA). The mean authentication accuracies of EC, EO, and Diff were 97.05 ± 5.4, 92.5 ± 8.2, and 85.3 ± 7.0 %, respectively, demonstrating the feasibility of single-trial-based EEG authentication. EC could be an optimal condition for developing a resting-state EEG authentication system based on single trial.