Person Authentication Using Brainwaves (EEG) and Maximum A Posteriori Model Adaptation

Abstract

In this paper, we investigate the use of brain activity for person authentication. It has been shown in previous studies that the brain-wave pattern of every individual is unique and that the electroencephalogram (EEG) can be used for biometric identification. EEG-based biometry is an emerging research topic and we believe that it may open new research directions and applications in the future. However, very little work has been done in this area and was focusing mainly on person identification but not on person authentication. Person authentication aims to accept or to reject a person claiming an identity, i.e., comparing a biometric data to one template, while the goal of person identification is to match the biometric data against all the records in a database. We propose the use of a statistical framework based on Gaussian Mixture Models and Maximum A Posteriori model adaptation, successfully applied to speaker and face authentication, which can deal with only one training session. We perform intensive experimental simulations using several strict train/test protocols to show the potential of our method. We also show that there are some mental tasks that are more appropriate for person authentication than others.

I Think, Therefore I Am: Usability and Security of Authentication Using Brainwaves

“…We are able to maintain a high level of authentication accuracy with a subject pool that is 66% to 275% larger than those from previous studies [3,12,[14][15][16], thus demonstrating the feasibility of authentication in a small population, e.g., a work group setting [14]. Nonetheless, it would still be valuable to investigate the scalability of the results to even larger populations.…”

“…Poulos et al use an artificial neural network to classify 4 subjects based on their EEG signals [16]. Marcel and Millan employ gaussian mixture model and maximum a-posteriori model for authentication with 9 subjects [12]. Palaniappan achieved 100% accuracy in classifying 5 subjects using a linear discriminant classifier [14], as well as zero False Acceptance Rate (FAR) and zero False Rejection Rate (FRR) using a two-stage thresholdbased authentication process [15].…”

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.…”

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).…”