  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.
