Abstract-The use of EEG biometrics, for the purpose of automatic people recognition, has received increasing attention in the recent years. Most of current analysis rely on the extraction of features characterizing the activity of single brain regions, like power-spectrum estimates, 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 coherencebased 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 performances show 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 97.41% is obtained in EC (96.26% in EO) when fusing power spectrum information from centro-parietal regions. Taken together, these results suggest that functional connectivity patterns represent effective features for improving EEG-based biometric systems.

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Brain signals have been investigated for more than a century in the medical field. However, despite the broad interest in clinical applications, their use as a biometric identifier has been only recently considered by the scientific community. In this paper, we focus on the permanence across time of brain signals, specifically of electroencephalographic (EEG) signals, issue of paramount importance for the deployment of brain-based biometric recognition systems in real life, not yet fully addressed. In particular, we speculate about the stability of EEG features by analyzing the recognition performance that can be achieved when comparing EEG signals acquired during different sessions. We carry out an extensive set of experimental tests, performed on several EEG-based biometric systems over a large database,\ud comprising three recordings taken from 50 healthy subjects in resting state conditions, acquired in a time span of approximately one month and a half. The results confirm that a significant level of permanence can be guaranteed