# How Time Window Influences Biometrics Performance: An EEG-Based Fingerprint Connectivity Study
## 4 Discussion  
In the present study, we investigated the effect of the time window on a hypothetical EEG-based biometric system for user authentication. The rationale for this study is derived from the evidence where it has been previously reported that the definition of the EEG  
Figure 4: The effect of using different time windows, from 0.5 s to 12 s, with a step of 0.5 s, on the EER (**left** panel) and AUC (**right** panel) for the PLV in the gamma frequency band using the DREAMER EEG baseline traces. Figure 3: The score distributions for the two more extreme windows, the shorter of 0.5 s and the larger of 12 s, for the PLV methods in the gamma frequency band for the eyes-open resting state condition. time window may represent a crucial issue in the EEG analysis [18] and that the length of the time window may strongly affect several EEG features, especially those based on connectivity methods [19, 20]. In particular, these studies have highlighted that shorter epochs show less clear and more blurred connectivity patterns when compared with those obtained for longer epochs. Altogether, these studies suggest that the size of the EEG segment to be considered when developing an EEG-based biometric system may play a key role and have a relevant effect on the final findings. Furthermore, deriving the optimal time window in an EEG biometric system would allow us to define the minimum required amount of time necessary to have the system properly working with satisfactory performance, thus avoiding including in the analysis longer segments of EEG recordings. This latest issue is particularly relevant in the resting state condition when drowsiness may occur, causing changes in the derived EEG characteristics. To test this research question, we used two well-known, widely used connectivity metrics shown to have good aptitude as subject-specific fingerprints in a resting state EEG [11, 15]. The analysis was performed on two different freely available EEG datasets to make the study more reproducible. Overall, as hypothesized, the reported results showed a pronounced effect of the time window on the biometric performance measured in terms of both the EER and AUC. In particular, we observed that the EER varied from 0.478 for the smaller time window to 0.018 for the larger, meaning that for short EEG recording segments, it is impossible to extract connectivity features able to detect any subject distinctive characteristic of the original signal. In contrast, for longer segments of the EEG signal, regardless of the connectivity metric and the dataset, our results confirm previous studies that reported very high performance in terms of individual authentication using an EEG-based system. The same output was observed for the AUC, which, again, varied from 0.476 for the smaller window to 0.002 for the larger one. In summary, these findings suggest that it is possible to achieve very high performance levels even with relatively short EEG time windows, specifically in the order of 10 s. This result has significant implications for the development of future biometric systems, as it implies that such systems could be designed to function effectively with minimal EEG recording durations. By utilizing shorter EEG recordings, the overall quantity of data that needs to be acquired, stored, and processed can be substantially reduced, thereby enhancing the efficiency and practicality of these systems in real-world applications. In our opinion, these results align with previous studies [20] that have reported the effect of epoch length on connectivity metrics and suggest an optimal time window (around 10 s) necessary to develop future EEG-based biometric systems that use connectivity metrics to extract the features from the original recordings. This optimal window is likely crucial for the development of future EEG-based biometric systems, where the balance between data sufficiency and recording duration is critical.