2023/07/17 by Luca Didaci, Sara Maria Pani, Didaci, Luca +5
Neuroscience · #EEG and Brain-Computer Interfaces #FOS: Electrical engineering #Functional Brain Connectivity Studies #Neural dynamics and brain function #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2307.08291
openalex publication_date 2023/07/17 · openalex created_date 2023/07/20 · openalex updated_date 2026/07/28
EEG-based biometric represents a relatively recent research field that aims to recognize individuals based on their recorded brain activity by means of electroencephalography (EEG). Among the numerous features that have been proposed, connectivity-based approaches represent one of the more promising methods tested so far. In this paper, we investigate how the performance of an EEG biometric system varies with respect to different time windows to understand if it is possible to define the optimal duration of EEG signal that can be used to extract those distinctive features. Overall, the results have shown a pronounced effect of the time window on the biometric performance measured in terms of EER (equal error rate) and AUC (area under the curve), with an evident increase of the biometric performance with an increase of the time window. In conclusion, we want to highlight that EEG connectivity has the potential to represent an optimal candidate as EEG fingerprint and that, in this context, it is very important to define a sufficient time window able to collect the subject specific features. Moreover, our preliminary results show that extending the window size beyond a certain maximum does not improve biometric systems' performance.