Katsigiannis, Stamos and Arnau-González, Pablo and Arevalillo-Herráez, Miguel and Ramzan, Naeem (2021) 'Single-channel EEG-based subject identification using visual stimuli.', 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) Online, 27-30 July 2021.
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.
|Item Type:||Conference item (Paper)|
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||https://doi.org/10.1109/BHI50953.2021.9508581|
|Publisher statement:||© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
|Date accepted:||08 June 2021|
|Date deposited:||08 November 2021|
|Date of first online publication:||10 August 2021|
|Date first made open access:||08 November 2021|
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