Identifying Individuals by fNIRS-Based Brain Functional Network Fingerprints - Ren, Haonan; Zhou, Shufeng; Zhang, Limei; Zhao, Feng; Qiao, Lishan (Frontiers in Neuroscience, 2022)
Individual identification based on brain functional network (BFN) has attracted a lot of research interest in recent years, since it provides a novel biometric for identity authentication, as well as a feasible way of exploring the brain at an individual level. Previous studies have shown that an individual can be identified by its BFN fingerprint estimated from functional magnetic resonance imaging (fMRI), electroencephalogram (EEG) or magnetoencephalography (MEG) data. In consideration of the fact that functional near-infrared spectroscopy (fNIRS) is an emerging imaging technique that, by measuring the changes in blood oxygen concentration, can respond to cerebral activities, in this paper, we investigate whether fNIRS-based BFN could be used as a ``fingerprint" to identify individuals. In particular, Pearson’s correlation (PC) is first used to calculate BFN based on the preprocessed fNIRS signals, and then the nearest neighbor scheme is used to match the estimated BFNs between different individuals. Through the experiments on an open-access fNIRS dataset, we have two main findings: 1) the fNIRS-based BFN fingerprint can serve as an alternative biometric for identifying individual with near-perfect performance, and, more interestingly, 2) even under the cases of cross-task (i.e., right-handed, left-handed finger tapping and foot tapping), the BFN fingerprints generally work well for the individual identification.