EEG personal recognition based on ‘qualified majority’ over signal patches

Abstract

Electroencephalography (EEG)-based personal recognition in realistic contexts is still a matter of research, with the following issues to be clarified: (1) the duration of the signal length, called ‘epoch’, which must be very short for practical purposes and (2) the contribution of EEG sub-bands. These two aspects are connected because the shorter the epoch’s duration, the lower the contribution of the low-frequency sub-bands while enhancing the high-frequency sub-bands. However, it is well known that the former characterises the inner brain activity in resting or unconscious states. These sub-bands could be of no use in the wild, where the subject is conscious and not in the condition to put himself in a resting-state-like condition. Furthermore, the latter may concur much better in the process, characterising normal subject activity when awake. This study aims at clarifying the problems mentioned above by proposing a novel personal recognition architecture based on extremely short signal fragments called ‘patches’, subdividing each epoch. Patches are individually classified. A ‘qualified majority’ of classified patches allows taking the final decision. It is shown by experiments that this approach (1) can be adopted for practical purposes and (2) clarifies the sub-bands’ role in contexts still implemented in vitro but very similar to that conceivable in the wild.