Single-channel EEG-based subject identification using visual stimuli

Katsigiannis, Stamos; Arnau-González, Pablo; Arevalillo-Herráez, Miguel; Ramzan, Naeem

doi:10.1109/bhi50953.2021.9508581

Single-channel EEG-based subject identification using visual stimuli

Katsigiannis, Stamos; Arnau-González, Pablo; Arevalillo-Herráez, Miguel; Ramzan, Naeem

Authors

Dr Stamos Katsigiannis stamos.katsigiannis@durham.ac.uk
Assistant Professor

Pablo Arnau-González

Miguel Arevalillo-Herráez

Naeem Ramzan

Abstract

Electroencephalography (EEG) signals have been recently proposed as a biometrics modality due to some inherent advantages over traditional biometric approaches. In this work, we studied the performance of individual EEG channels for the task of subject identification in the context of EEG-based biometrics using a recently proposed benchmark dataset that contains EEG recordings acquired under various visual and non-visual stimuli using a low-cost consumer-grade EEG device. Results showed that specific EEG electrodes provide consistently higher identification accuracy regardless of the feature and stimuli types used, while features based on the Mel Frequency Cepstral Coefficients (MFCC) provided the highest overall identification accuracy. The detection of consistently well-performing electrodes suggests that a combination of fewer electrodes can potentially provide efficient identification performance, allowing the use of simpler and cheaper EEG devices, thus making EEG biometrics more practical.

Citation

Katsigiannis, S., Arnau-González, P., Arevalillo-Herráez, M., & Ramzan, N. (2021). Single-channel EEG-based subject identification using visual stimuli. . https://doi.org/10.1109/bhi50953.2021.9508581

Conference Name	2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)
Conference Location	Online
Start Date	Jul 27, 2021
End Date	Jul 30, 2021
Acceptance Date	Jun 8, 2021
Online Publication Date	Aug 10, 2021
Publication Date	2021
Deposit Date	Jun 8, 2021
Publicly Available Date	Nov 8, 2021
Publisher	Institute of Electrical and Electronics Engineers
DOI	https://doi.org/10.1109/bhi50953.2021.9508581

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