This paper deals with electroencephalography (EEG)-based biometric identification, using a motor imagery task, specifically performing imaginary arms and legs movements. Deep learning methods such as convolutional neural network (CNN) is used for automatic discriminative feature extraction and person identification. An extensive set of experimental tests, performed on a large database comprising EEG data collected from 40 subjects over two different sessions taken at a week distance, shows the existence of repeatable discriminative characteristics in individuals' brain signals.

Das, R., Maiorana, E., Campisi, P. (2018). Motor Imagery for EEG Biometrics Using Convolutional Neural Network. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp.2062-2066). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICASSP.2018.8461909].

Motor Imagery for EEG Biometrics Using Convolutional Neural Network

Das, Rig;Maiorana, Emanuele;Campisi, Patrizio
2018-01-01

Abstract

This paper deals with electroencephalography (EEG)-based biometric identification, using a motor imagery task, specifically performing imaginary arms and legs movements. Deep learning methods such as convolutional neural network (CNN) is used for automatic discriminative feature extraction and person identification. An extensive set of experimental tests, performed on a large database comprising EEG data collected from 40 subjects over two different sessions taken at a week distance, shows the existence of repeatable discriminative characteristics in individuals' brain signals.
2018
9781538646588
Das, R., Maiorana, E., Campisi, P. (2018). Motor Imagery for EEG Biometrics Using Convolutional Neural Network. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp.2062-2066). Institute of Electrical and Electronics Engineers Inc. [10.1109/ICASSP.2018.8461909].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/347535
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