Biometrics from Brain Electrical Activity: A Machine Learning Approach

Abstract

The potential of brain electrical activity generated as a response to a visual stimulus is examined in the context of the identification of individuals. Specifically, a framework for the Visual Evoked Potential (VEP)-based biometrics is established, whereby energy features of the gamma band within VEP signals were of particular interest. A rigorous analysis is conducted which unifies and extends results from our previous studies, in particular, with respect to 1) increased bandwidth, 2) spatial averaging, 3) more robust power spectrum features, and 4) improved classification accuracy. Simulation results on a large group of subject support the analysis.

Bioelectrical Signals as Emerging Biometrics: Issues and Challenges

“…Bioelectrical signals especially, the ECG and the EEG are emerging biometric identities [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Unlike anatomical biometric identities that have two-dimensional data representation, the ECG or EEG is physiologically low-frequency signals that have one-dimensional data representation.…”

“…The studies suggest that the brain electrical activity of an individual is unique and EEG can be used as a new biometric for people identity verification [12][13][14][15][16][17]. Although, the data acquisition of the EEG is somewhat cumbersome.…”

“…Palaniappan and Mandic [14] have examined the energy of brain potentials evoked during processing of visual stimuli for individual identification. Following the study in [15], they have analyzed the potential of dominant frequency powers in gamma band visual evoked potential (VEP) signals as a biometric.…”

“…However, the next step is to test the viability of the EEG as biometric over larger data sets where different mental states under different scenarios need to be investigated. Although, the study in [14] has experimented a total of 3,560 VEP signals collected from 102 subjects that are free from eye-induced artifacts, the feasibility of EEG data collected under multiple brain states including REM and NREM sleep to assess the performance for individual authentication over large data sets is yet to be addressed.…”

Exploiting the P300 paradigm for cognitive biometrics

“…Of late, there has been a spurt of activity to exploit EEG for authentication (Ravi and Palaniappan, 2006;Marcel and Millan, 2007;Palaniappan and Mandic, 2007a;Palaniappan and Mandic, 2007b;Palaniappan, 2008;Riera et al, 2008) in addition to others physiological biometrics like Electrocardiogram (ECG) (Palaniappan and Krishnan, 2004). Multiple signal classification (MUSIC) algorithm was used to classify energy features within gamma band (Palaniappan and Mandic, 2007a), Elman neural network with spatial data/sensor fusion (Palaniappan and Mandic, 2007b), singe trials of non-time locked evoked potentials (Ravi and Palaniappan, 2006), non-linear features from simple mental tasks (Palaniappan, 2008), power spectral density feature with Gaussian mixture models (Marcel and Millan, 2007) and a multi-feature (Riera et al, 2008) approaches were used for person authentication in these studies. ERD/ERS pattern was also identified as a possible stable biometric marker, in a BCI context (Pfurtscheller and Neuper 2006).…”