An increased level of attention has recently raised on biometric recognition by means of electroencephalography (EEG). This modality in fact possesses several properties which may be appealing for automatic people recognition, such as the intrinsic liveness detection and the robustness against potential attacks. Moreover, it could be easily exploited in applications based on brain-computer interfaces (BCI). In this paper we exhaustively analyze the discriminative capability of a compact representation of EEG signals acquired in resting conditions. Specifically, the exploited templates are obtained as projections into a subspace defined through EigenBrains (EBs), a basis for EEG data relying on principal component analysis (PCA). An extensive set of experimental tests, conducted on a database comprising 60 users, is performed to evaluate the recognition capabilities of the proposed representation under different system configurations.
Maiorana, E., LA ROCCA, D., Campisi, P. (2015). EEG-based biometric recognition using EigenBrains. In 2015 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2015 (pp.1-6). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICMEW.2015.7169794].
EEG-based biometric recognition using EigenBrains
MAIORANA, EMANUELE;LA ROCCA, DARIA;CAMPISI, PATRIZIO
2015-01-01
Abstract
An increased level of attention has recently raised on biometric recognition by means of electroencephalography (EEG). This modality in fact possesses several properties which may be appealing for automatic people recognition, such as the intrinsic liveness detection and the robustness against potential attacks. Moreover, it could be easily exploited in applications based on brain-computer interfaces (BCI). In this paper we exhaustively analyze the discriminative capability of a compact representation of EEG signals acquired in resting conditions. Specifically, the exploited templates are obtained as projections into a subspace defined through EigenBrains (EBs), a basis for EEG data relying on principal component analysis (PCA). An extensive set of experimental tests, conducted on a database comprising 60 users, is performed to evaluate the recognition capabilities of the proposed representation under different system configurations.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
