Identifying Individuals by fNIRS-Based Brain Functional Network Fingerprints

Abstract

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, electroencephalogram, or magnetoencephalography data. 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 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) under the cases of cross-task (i.e., resting, right-handed, left-handed finger tapping, and foot tapping), the BFN fingerprints generally work well for the individual identification, and, more interestingly, (2) the accuracy under cross-task is well above the accuracy under cross-view (i.e., oxyhemoglobin and de-oxyhemoglobin). These findings indicate that fNIRS-based BFN fingerprint is a potential biometric for identifying individual.

Identification of individuals using functional near‐infrared spectroscopy based on a one‐dimensional convolutional neural network

“…In the domain of individual identification, fNIRS also exhibits relevant applications. The extraction of biologically specific brain functional networks from fNIRS has been demonstrated to be a potential biomarker for recognizing individual physiological characteristics [25]. Simultaneously, given the significant progress achieved in individual recognition research through functional connectivity based on fMRI [26], the question of the potential feasibility of utilizing functional connectivity based on fNIRS in individual identification studies has been raised.…”

Revealing the spatiotemporal requirements for accurate subject identification with resting-state functional connectivity: a simultaneous fNIRS-fMRI study

“… 29 , 30 A better understanding of what drives subject identification could allow us to differentiate between participant- and session-dependent brain connectivity patterns, which is critical for isolating longitudinal brain changes from spurious fluctuations of the measured signal. Despite its potential, only a few works have attempted to perform brain fingerprinting with fNIRS to date, 31 , 32 which is probably related to the fact that fMRI methods are not directly translated to fNIRS due to the intrinsic noise properties of the latter. Specifically, fNIRS’ high temporal resolution allows for the removal of systemic physiological noise, and it samples the brain faster than the hemodynamic changes and leads to high temporal autocorrelation in the hemoglobin time series.…”