Leave-one-out Authentication of Persons Using 40 Hz EEG Oscillations

Abstract

It has been shown previously that recognizing persons using 40 Hz electroencephalogram (EEG) oscillations is possible. In the method, features were computed from the Visual Evoked Potential (VEP) signals recorded from 61 electrodes while subjects perceived a picture. Here, two modifications have been proposed to improve the classification performance: Principal Component Analysis (PCA) to reduce the noise and background EEG effects from the VEP signals and normalization. Two classifiers were used: Simplified Fuzzy ARTMAP (SFA), and k-Nearest Neighbor (kNN). The experimental results using 800 VEP signals from 20 subjects with leave-one-out cross validation strategy showed that PCA and normalization improved the classification performance for both the classifiers. The best classification performance of 95.25% obtained using the improved method shows that 40 Hz EEG oscillations are suitable for use as biometrics.

Single Trial Authentication with Mental Password Writing

“…They have reached a mean accuracy of 86.1 %. While Ravi [6] and Zúquete [7] have presented black and white pictures from Snodgrass and Vanderwart picture set to 70 individuals. Ravi has achieved an identification accuracy of 95.25 % using 40 Hz EEG oscillations.…”

EEG Based Brain Computer Interface for Speech Communication: Principles and Applications

“…The brain state of individuals used for the authentication mechanism increases the robustness and enable cross validation when used in combination with traditional biometric methods. The biometric approaches based on the biological features of humans [42,52,62,63,[69][70][71] have distinct advantages over traditional methods. The cognitive biometric cannot be hacked, stolen or transferred from one person to another as they are unique for each person.…”

Biometric Authentication Using Brain Responses to Visual Stimuli

“…Using VEPs and signals in the γ band to perform subject identification was proposed by Palaniappan (Palaniappan, 2004) and followed on his posterior studies (Palaniappan and Mandic, 2005;Ravi and Palaniappan, 2005b;Ravi and Palaniappan, 2005a;Ravi and Palaniappan, 2006;Palaniappan and Mandic, 2007). In all these works is used the same dataset of VEPs, recorded from 40 individuals and comprising a 61-channel EEG for 30 VEPs triggered by pictures chosen from the Snodgrass & Vanderwart set.…”

“…For filtering, in (Palaniappan, 2004;Ravi and Palaniappan, 2005a;Ravi and Palaniappan, 2006) a Butterworth filter was used, while in (Ravi and Palaniappan, 2005b;Palaniappan and Mandic, 2005;Palaniappan and Mandic, 2007) an elliptic FIR filter was used (in the latter the lower pass-band threshold was lower, 20 Hz). For classifying, in (Ravi and Palaniappan, 2005b;Palaniappan and Mandic, 2005;Ravi and Palaniappan, 2006;Palaniappan and Mandic, 2007) was used an Elman back-propagation neural network (Elman, 1990), in (Palaniappan, 2004) a back-propagation multi-layer perceptron, in (Ravi and Palaniappan, 2005a;Ravi and Palaniappan, 2006) a simplified fuzzy ARTMAP (Kasuba, 1993) and in (Ravi and Palaniappan, 2005a) …”

“…For classifying, in (Ravi and Palaniappan, 2005b;Palaniappan and Mandic, 2005;Ravi and Palaniappan, 2006;Palaniappan and Mandic, 2007) was used an Elman back-propagation neural network (Elman, 1990), in (Palaniappan, 2004) a back-propagation multi-layer perceptron, in (Ravi and Palaniappan, 2005a;Ravi and Palaniappan, 2006) a simplified fuzzy ARTMAP (Kasuba, 1993) and in (Ravi and Palaniappan, 2005a) …”