EEG-based personal identification: comparison of different functional connectivity metrics

Abstract

Growing interest is devoted to understanding how brain signals recorded from scalp electroencephalography (EEG) may represent unique fingerprints of individual neural activity. In this context, the present paper aims to investigate the impact of some of the most commonly used techniques to estimate functional connectivity on the ability to unveil personal distinctive patterns of inter-regional interactions. Different metrics, commonly used to estimate functional connectivity and derived centrality measures, were compared in terms of equal error rate. It is widely accepted that each metric carries specific information in respect to the underlying interactions network. Nevertheless, the reason why these metrics convey different subject specific information has not been investigated yet. Experimental results on two publicly available datasets suggest that different functional connectivity metrics define a peculiar subjective profile of connectivity and have different mechanisms to detect subject-specific patterns of inter-channel interactions. It is important to consider the effects that frequency content and spurious connectivity values may play in determining subject-specific characteristics.