{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:47:46Z","timestamp":1770338866334,"version":"3.49.0"},"reference-count":50,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T00:00:00Z","timestamp":1647734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Higher Education of Russia","award":["FEWM-2020-0037 (TUSUR)"],"award-info":[{"award-number":["FEWM-2020-0037 (TUSUR)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>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\u2019s main advantages are subject independence and suitability for interpretation via created spectrograms and the integrated gradients method.<\/jats:p>","DOI":"10.3390\/computers11030047","type":"journal-article","created":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T21:26:22Z","timestamp":1647811582000},"page":"47","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Representation Learning for EEG-Based Biometrics Using Hilbert\u2013Huang Transform"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5060-0958","authenticated-orcid":false,"given":"Mikhail","family":"Svetlakov","sequence":"first","affiliation":[{"name":"Department of Complex Information Security of Computer Systems, Faculty of Security, Tomsk State University of Control Systems and Radioelectronics, 634000 Tomsk, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2054-717X","authenticated-orcid":false,"given":"Ilya","family":"Kovalev","sequence":"additional","affiliation":[{"name":"Department of Complex Information Security of Computer Systems, Faculty of Security, Tomsk State University of Control Systems and Radioelectronics, 634000 Tomsk, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3222-9956","authenticated-orcid":false,"given":"Anton","family":"Konev","sequence":"additional","affiliation":[{"name":"Department of Complex Information Security of Computer Systems, Faculty of Security, Tomsk State University of Control Systems and Radioelectronics, 634000 Tomsk, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8000-2716","authenticated-orcid":false,"given":"Evgeny","family":"Kostyuchenko","sequence":"additional","affiliation":[{"name":"Department of Complex Information Security of Computer Systems, Faculty of Security, Tomsk State University of Control Systems and Radioelectronics, 634000 Tomsk, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2624-4383","authenticated-orcid":false,"given":"Artur","family":"Mitsel","sequence":"additional","affiliation":[{"name":"Department of Automated Control Systems, Faculty of Control Systems, Tomsk State University of Control Systems and Radioelectronics, 634000 Tomsk, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,20]]},"reference":[{"key":"ref_1","first-page":"113","article-title":"Biometric Authentication: A Review","volume":"2","author":"Kodituwakku","year":"2015","journal-title":"Int. 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