| Autor: |
Mikhail Svetlakov, Ilya Kovalev, Anton Konev, Evgeny Kostyuchenko, Artur Mitsel |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Computers, Vol 11, Iss 3, p 47 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2073-431X |
| DOI: |
10.3390/computers11030047 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
