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

Статья в журнале

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.

Журнал:

• Computers
• MDPI (Basel)
• Индексируется в Scopus

Библиографическая запись: Representation Learning for EEG-Based Biometrics Using Hilbert–Huang Transform [Electronic resource] / M. Svetlakov [et al.] // Computers. – 2022. – Vol. 11. – Iss. 3. – P. 47. – DOI 10.3390/computers11030047

Индексируется в:

• Scopus ( https://www.scopus.com/record/display.uri?eid=2-s2.0-85127427673&origin=resultslist&sort=plf-f )
• РИНЦ ( https://www.elibrary.ru/item.asp?id=48424150 )