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Single-channel EEG-based subject identification using visual stimuli

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

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Pablo Arnau-González

Miguel Arevalillo-Herráez

Naeem Ramzan


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.


Katsigiannis, S., Arnau-González, P., Arevalillo-Herráez, M., & Ramzan, N. (2021). Single-channel EEG-based subject identification using visual stimuli. .

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


Accepted Conference Proceeding (494 Kb)

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