# How Time Window Influences Biometrics Performance: An EEG-Based Fingerprint Connectivity Study
## 1 Introduction  
EEG-based biometrics represent a relatively recent research field that aims to recognize individuals based on their recorded brain activity using electroencephalography (EEG). Even though it is still in the development phase, several reviews [1; 2; 3; 4; 5] have already been published, which have the merit of describing in detail the evolutions, progress, and achievements of this promising research theme. EEG-based biometrics are strictly related to the more specific topic of evaluating individual fingerprints, which involves using several different neuroimaging techniques, such as functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG), of a human connectome [6]. During the last few years, there has been growing evidence that EEG signals, which reflect neural responses to various kinds of stimuli, including resting state activity, can be successfully used to derive distinctive features to develop high-performance, secure, and robust biometric recognition systems [2; 5; 7; 8], thus representing a possible new method to more classical individual recognition and authentication approaches [9]. In the context of the EEG biometric system, among the numerous features proposed to investigate how the EEG signals behave in terms of brain fingerprint,connectivity-based approaches [10] represent one of the more promising methods tested so far [11; 12; 13; 14]. In particular, phase-based connectivity methods, which allow the estimation of the correlation between different EEG channels (or the corresponding reconstructed sources) based on the phase synchronization of EEG signals, outperformed other connectivity and power-based approaches [11; 15], thus representing a valid solution to develop high-performance EEG biometric systems. In this context, the PLI (phase lag index) [16] and the PLV (phase locking value) [17] connectivity methods have shown to be good candidates as EEG-based features to recognize individuals in several experimental scenarios [11; 12; 15]. Despite their broad application and inherent advantages over more common biometrics, EEG-based biometric systems still have some severe drawbacks, including, but not limited to, the need for standardized pipelines for the analysis [2]. In particular, it has been previously reported that the definition of the EEG time window represents a crucial issue in the EEG analysis [18] and should be considered a fundamental point when defining experimental protocols for MEG and EEG studies. More specifically, the time window length, namely the epoch length, may strongly affect several EEG features, especially those based on connectivity methods [19; 20], both at the scalp and source level. These studies have highlighted that shorter epochs show less clear and more blurred connectivity patterns when compared with those obtained for longer epochs. On the other hand, understanding the optimal time window in an EEG biometric system is of huge interest because it allows one to define, at least for brain signals, the minimum required amount of time necessary to have the system properly working with satisfactory performance. In this paper, we investigate how the performance of an EEG biometric system, based on two commonly and widely used phase connectivity methods, varies with respect to different time windows with the aim to understand if, at least for EEG fingerprint approaches, it is possible to define the optimal duration of EEG signal that can be used to extract those distinctive features. We evaluated our hypothesis using a public EEG dataset [21] consisting of recordings from 109 subjects acquired with a high-density EEG system that can be downloaded at the following link: ([[http://physionet.org/pn4/eegmmidb/](http://physionet.org/pn4/eegmmidb/)]([http://physionet.org/pn4/eegmmidb/](http://physionet.org/pn4/eegmmidb/)), accessed on 28 May 2024). The analysis was performed using both eyes-closed and eyes-open resting state conditions, which have been previously shown to be able to develop high-performance EEG biometric systems [12].