Human Brain Distinctiveness Based on EEG Spectral Coherence Connectivity

• Published in: IEEE transactions on biomedical engineering Vol. 61; no. 9; pp. 2406 - 2412
• Main Authors: Rocca, D. La, Campisi, P., Vegso, B., Cserti, P., Kozmann, G., Babiloni, F., Fallani, F. De Vico
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Accuracy; Biometric Identification; Biometrics; Biometrics (access control); Brain; Brain - physiology; Cognitive science; Coherence; Computer Science; Electrodes; Electroencephalography; Electroencephalography - methods; Engineering Sciences; Feature extraction; Humans; Machine Learning; Neuroscience; Signal and Image processing; Signal Processing, Computer-Assisted; Vectors; Biometrics; electroencephalography (EEG); resting state; spectral coherence; match score fusion; Resting state; Match score fusion; EEG; Spectral coherence
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2014.2317881

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.