Representation Learning for EEG-Based Biometrics Using Hilbert–Huang Transform
Mikhail Svetlakov,
Ilya Kovalev,
Anton Konev,
Evgeny Kostyuchenko,
Artur Mitsel
Affiliations
Mikhail Svetlakov
Department of Complex Information Security of Computer Systems, Faculty of Security, Tomsk State University of Control Systems and Radioelectronics, 634000 Tomsk, Russia
Ilya Kovalev
Department of Complex Information Security of Computer Systems, Faculty of Security, Tomsk State University of Control Systems and Radioelectronics, 634000 Tomsk, Russia
Anton Konev
Department of Complex Information Security of Computer Systems, Faculty of Security, Tomsk State University of Control Systems and Radioelectronics, 634000 Tomsk, Russia
Evgeny Kostyuchenko
Department of Complex Information Security of Computer Systems, Faculty of Security, Tomsk State University of Control Systems and Radioelectronics, 634000 Tomsk, Russia
Artur Mitsel
Department of Automated Control Systems, Faculty of Control Systems, Tomsk State University of Control Systems and Radioelectronics, 634000 Tomsk, Russia
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.
