On the invariance of EEG-based signatures of individuality with application in biometric identification.

One of the main challenges in EEG-based biometric systems is to extract reliable signatures of individuality from recorded EEG data that are also invariant against time. In this paper, we investigate the invariability of features that are extracted based on the spatial distribution of the spectral power of EEG data corresponding to 2-second eyes-closed resting-state (ECRS) recording, in different scenarios. Eyes-closed resting-state EEG signals in 4 healthy adults are recorded in two different sessions with an interval of at least one week between sessions. The performance in terms of correct recognition rate (CRR) is examined when the training and testing datasets are chosen from the same recording session, and when the training and testing datasets are chosen from different sessions. It is shown that an CRR of 92% can be achieved based on the proposed features when the training and testing datasets are taken from different sessions. To reduce the number of recording channels, principal component analysis (PCA) is also employed to identify channels that carry the most discriminatory information across individuals. High CRR is obtained based on the data from channels mostly covering the occipital region. The results suggest that features based on the spatial distribution of the spectral power of the short-time (e.g. 2 seconds) ECRS recordings can have great potentials in EEG-based biometric identification systems.