On the Repeatability of EEG Features in a Biometric Recognition Framework using a Resting State Protocol

Daria La Rocca, Patrizio Campisi, Gaetano Scarano

2013

Abstract

In this paper the feasibility of the electroencephalogram (EEG) as biometric identifier is investigated with focus on the repeatability of the EEG features employed in the proposed framework. The use of EEG within the biometric framework has already been introduced in the recent past although it has not been extensively analyzed. In this contribution we infer about the invariance over time of the employed EEG features, which is one of the most relevant properties a biometric identifier should possess in order to be employed in real life applications. For the purpose of this study we rely on the “resting state” protocol. The employed database is composed by healthy subjects whose EEG signals have been acquired in two different sessions. Different electrodes configurations pertinent to the employed protocol have been considered. Autoregressive statistical modeling using reflection coefficients has been adopted and a linear classifier has been tested. The obtained results show that a high degree of repeatability has been achieved over the considered interval.

Paper Citation

in Harvard Style

La Rocca D., Campisi P. and Scarano G. (2013). On the Repeatability of EEG Features in a Biometric Recognition Framework using a Resting State Protocol . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: MPBS, (BIOSTEC 2013) ISBN 978-989-8565-36-5, pages 419-428. DOI: 10.5220/0004339104190428

in Bibtex Style

@conference{mpbs13,
author={Daria La Rocca and Patrizio Campisi and Gaetano Scarano},
title={On the Repeatability of EEG Features in a Biometric Recognition Framework using a Resting State Protocol},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: MPBS, (BIOSTEC 2013)},
year={2013},
pages={419-428},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004339104190428},
isbn={978-989-8565-36-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: MPBS, (BIOSTEC 2013)
TI - On the Repeatability of EEG Features in a Biometric Recognition Framework using a Resting State Protocol
SN - 978-989-8565-36-5
AU - La Rocca D.
AU - Campisi P.
AU - Scarano G.
PY - 2013
SP - 419
EP - 428
DO - 10.5220/0004339104190428