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Robust Single-Trial EEG-Based Authentication Achieved with a 2-Stage Classifier by Uladzislau Barayeu; Nastassya Horlava; Arno Libert; Marc Van Hulle is a Engineering article available to read on EtoBox.

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The risk of personal data exposure through unauthorized access has never been as imminent as today. To counter this, biometric authentication has been proposed: the use of distinctive physiological and behavioral characteristics as a form of identification and access control. One of the recent developments is electroencephalography (EEG)-based authentication. It builds on the subject-specific nature of brain responses which are difficult to recreate artificially. We propose an authentication system based on EEG signals recorded in response to a simple motor paradigm. Authentication is achieved with a novel two-stage decoder. In the first stage, EEG signal features are extracted using an inception- and a VGG-like deep learning neural network (NN) both of which we compare with principal component analysis (PCA). In the second stage, a support vector machine (SVM) is used for binary classification to authenticate the subject based on the extracted features. All decoders are trained on EEG motor-movement data recorded from 105 subjects. We achieved with the VGG-like NN-SVM decoder a false-acceptance rate (FAR) of 2.55% with an overall accuracy of 88.29%, a FAR of 3.33% with an accuracy

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Author

Uladzislau Barayeu; Nastassya Horlava; Arno Libert; Marc Van Hulle

Publisher

MDPI AG

Published

2020

Language

EN

Field

Engineering (Physical Sciences)