Learning deep features for task-independent EEG-based biometric verification

Considerable interest has been recently devoted to the exploitation of brain activity as biometric identifier in automatic recognition systems, with a major focus on data acquired through electroencephalography (EEG). Several researches have in fact confirmed the presence of discriminative characteristics within brain signals recorded while performing specific mental tasks. Yet, to make EEG-based recognition appealing for practical applications, it would be highly advisable to investigate the existence and permanence of such distinctive traits while performing different mental tasks. In this regard, the present study evaluates the feasibility of performing task-independent EEG-based biometric recognition. A deep learning approach using siamese convolutional neural networks is employed to extract, from the considered EEG recordings, subject-specific template representations. An extensive set of experimental tests, performed on a multi-session database comprising EEG data acquired from 45 subjects while performing six different tasks, is employed to evaluate whether it is actually possible to verify the identity of a subject using brain signals, regardless the performed mental task. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This research evaluates the feasibility of task-independent EEG-based biometric recognition by utilizing a deep learning approach to extract subject-specific template representations. Through extensive experimental tests on a multi-session database, it is found that it is possible to verify the identity of a subject using brain signals regardless of the performed mental task.