Single-channel EEG-based subject identification using visual stimuli

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)
16 Downloads (Pure)


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.
Original languageEnglish
Title of host publication2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)
Place of PublicationPiscataway, NJ
Number of pages4
ISBN (Electronic)9781665403580
ISBN (Print)9781665447706
Publication statusPublished - 10 Aug 2021

Publication series

NameIEEE Conference Proceedings
ISSN (Print)2641-3590
ISSN (Electronic)2641-3604


  • EEG
  • biometrics
  • visual stimulus


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