Identifying Individuality Using Mental Task Based Brain Computer Interface Ramaswamy Palaniappan Dept. of Computer Science, University of Essex, Colchester, United Kingdom rpalan@essex.ac.uk; palani@iee.org Abstract In recent years, numerous Brain Computer Interface (BCI) technologies have been developed to assist the disabled. In this paper, mental task based BCI is proposed for a different purpose: to identify the individuality of a person. The idea is based on the classification of electroencephalogram (EEG) signals recorded when a user thinks of either one or two mental tasks. As different individuals have different thought processes, this idea would be appropriate for individual identification. To increase the inter-subject differences, EEG data from six electrodes are used instead of one. Sixth order autoregressive features are computed from EEG signals and classified by Linear Discriminant classifier using a modified 10 fold cross validation procedure, which gave an average error of 0.95% when tested on 400 EEG patterns from four subjects. Though the method would have to undergo further development to obtain repeatable good accuracy; this initial study has shown the huge potential of the method over existing biometric identification systems as it is impossible to be faked. 1. INTRODUCTION The most common biometric method of identifying an individual is through fingerprint recognition [1,2]. However, the individuality of fingerprints has been challenged [2]. Therefore, it becomes important to find alternative biometric methods to replace or augment the fingerprint technology. In this regard, other biometrics like palmprint [3], hand geometry [4], iris [5], face [6], and electrocardiogram [7] have been proposed. However, using EEG as a biometric is relatively new as compared to the other biometrics. Poulus et al [8] proposed a method using autoregressive (AR) modelling of EEG signals and Linear Vector Quantisation (LVQ) NN to classify an individual as distinct from other individuals with 72-80% success. But the method was not tried to recognise each individual in a group. Paranjape et al [9] used AR modelling of EEG with discriminant analysis to identify individuals with classification accuracy ranging from 49 to 85%. Palaniappan [10] proposed using Visual Evoked Potential recorded while the individuals perceive a single picture. However, this method required 61 channels, which is cumbersome and also required the individuals to perceive a visual stimulus, which is drawback for the visually impaired. In previous papers, it has been shown that mental task classification is a suitable technique for use in the design of Brain Computer Interfaces (BCIs) to aid the disabled to communicate or control devices [11-13]. BCIs are also useful for hands-off menu activation, which could be used by anyone. In this paper, mental task based BCI is proposed for a different application: to identify the individuality of the subjects. As far as the knowledge of the author is concerned, this is a novel BCI application. 2. DATA The EEG data used in this study were collected by Keirn and Aunon [11]. Data from four subjects were used in this study. The subjects were seated in an Industrial Acoustics Company sound controlled booth with dim lighting and noise-less fan (for ventilation). An Electro-Cap elastic electrode cap was used to record EEG signals from positions C3, C4, P3, P4, O1 and O2 (shown in Figure 1), defined by the 10-20 system [14] of electrode placement. The impedances of all electrodes were kept below 5 KΩ. Measurements were made with reference to electrically linked mastoids, A1 and A2. The electrodes were connected through a bank of amplifiers (Grass7P511), whose band-pass analog filters were set at 0.1 to 100 Hz. The data were sampled at 250 Hz with a Lab Master 12-bit A/D converter mounted on a computer. Before each recording session, the system was calibrated with a known voltage. EEG C4 C3 P3 P4 O1 O2 A1 A2 Fig. 1: Electrode placement Signals were recorded for 10s during each task and each task was repeated for 10 sessions where the sessions were held on different weeks. The EEG signal for each mental task was segmented into 20 segments with length 0.5 s. The sampling rate was 250 Hz, so each EEG segment was 125 data points (samples) in length.