EEG personal recognition based on ‘qualified majority’ over signal patches - Andrea Panzino; Giulia Orrù; Gian Luca Marcialis; Fabio Roli (IET Biometrics)
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.