Human brain distinctiveness based on EEG spectral coherence connectivity.
The use of EEG biometrics, for the purpose of automatic people recognition, has received increasing attention in the recent years. Most of the current analyses rely on the extraction of features characterizing the activity of single brain regions, like power spectrum estimation, thus neglecting possible temporal dependencies between the generated EEG signals. However, important physiological information can be extracted from the way different brain regions are functionally coupled. In this study, we propose a novel approach that fuses spectral coherence-based connectivity between different brain regions as a possibly viable biometric feature. The proposed approach is tested on a large dataset of subjects (N = 108) during eyes-closed (EC) and eyes-open (EO) resting state conditions. The obtained recognition performance shows that using brain connectivity leads to higher distinctiveness with respect to power-spectrum measurements, in both the experimental conditions. Notably, a 100% recognition accuracy is obtained in EC and EO when integrating functional connectivity between regions in the frontal lobe, while a lower 97.5% is obtained in EC (96.26% in EO) when fusing power spectrum information from parieto-occipital (centro-parietal in EO) regions. Taken together, these results suggest that the functional connectivity patterns represent effective features for improving EEG-based biometric systems.
Section of Applied Electronics, Department of Engineering, Università degli Studi ¿Roma Tre¿, Roma, Italy.
D La
D
Rocca
Section of Applied Electronics, Department of Engineering, Università degli Studi ¿Roma Tre¿, Roma, Italy.
P
P
Campisi
Department of Electrical Engineering and Information Systems, Faculty of Information Technology, University of Pannonia, Veszprem, Hungary.
B
B
Vegso
Department of Electrical Engineering and Information Systems, Faculty of Information Technology, University of Pannonia, Veszprem, Hungary.
P
P
Cserti
TAO, CNRS-INRIA-LRI, University of Paris Sud, Gif-sur-Yvette, France.
G
G
Kozmann
IRCCS Fondazione Santa Lucia, Rome, Italy.
F
F
Babiloni
UPMC Université Paris 06, UM75, Paris, France.
F De Vico
FV
Fallani
D056667:Biometric Identification; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D012815:Signal Processing, Computer-Assisted