Representation Learning for EEG-Based Biometrics Using Hilbert–Huang Transform

A promising approach to overcome the various shortcomings of password systems is the use of biometric authentication, in particular the use of electroencephalogram (EEG) data. In this paper, we propose a subject-independent learning method for EEG-based biometrics using Hilbert spectrograms of the d...

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Bibliographic Details
Published in:Computers
Main Authors: Mikhail Svetlakov, Ilya Kovalev, Anton Konev, Evgeny Kostyuchenko, Artur Mitsel
Format: Article
Language:English
Published: MDPI AG 2022-03-01
Subjects:
EEG
biometrics
multi-similarity loss
subject-independent
representation learning
Hilbert–Huang transform
Online Access:https://www.mdpi.com/2073-431X/11/3/47
Description
Summary:A promising approach to overcome the various shortcomings of password systems is the use of biometric authentication, in particular the use of electroencephalogram (EEG) data. In this paper, we propose a subject-independent learning method for EEG-based biometrics using Hilbert spectrograms of the data. The proposed neural network architecture treats the spectrogram as a collection of one-dimensional series and applies one-dimensional dilated convolutions over them, and a multi-similarity loss was used as the loss function for subject-independent learning. The architecture was tested on the publicly available PhysioNet EEG Motor Movement/Imagery Dataset (PEEGMIMDB) with a 14.63% Equal Error Rate (EER) achieved. The proposed approach’s main advantages are subject independence and suitability for interpretation via created spectrograms and the integrated gradients method.
ISSN:2073-431X

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