Towards Enhanced EEG-based Authentication with Motor Imagery Brain-Computer Interface

Bingkun Wu, Weizhi Meng, Wei-Yang Chiu

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Abstract

Electroencephalography (EEG) is the record of electrogram of the electrical activity on the scalp typically using non-invasive electrodes. In recent years, many studies started using EEG as a human characteristic to construct biometric identification or authentication. Being a kind of behavioral characteristics, EEG has its natural advantages whereas some characteristics have not been fully evaluated. For instance, we find that Motor Imagery (MI) brain-computer interface is mainly used for improving neurological motor function, but has not been widely studied in EEG authentication. Currently, there are many mature methods for understanding such signals. In this paper, we propose an enhanced EEG authentication framework with Motor Imagery, by offering a complete EEG signal processing and identity verification. Our framework integrates signal preprocess, channel selection and deep learning classification to provide an end-to-end authentication. In the evaluation, we explore the requirements of a biometric system such as uniqueness, permanency, collectability, and investigate the framework regarding insider and outsider attack performance, cross-session performance, and influence of channel selection. We also provide a large comparison with state-of-the-art methods, and our experimental results indicate that our framework can provide better performance based on two public datasets.
Original languageEnglish
Title of host publicationProceedings of the 38th Annual Computer Security Applications Conference
PublisherAssociation for Computing Machinery
Publication date2022
Pages799-812
ISBN (Print)978-1-4503-9759-9
DOIs
Publication statusPublished - 2022
Event38th Annual Computer Security Applications Conference - AT&T Conference Center, Austin, United States
Duration: 5 Dec 20229 Dec 2022
https://www.acsac.org/

Conference

Conference38th Annual Computer Security Applications Conference
LocationAT&T Conference Center
Country/TerritoryUnited States
CityAustin
Period05/12/202209/12/2022
Internet address

Keywords

  • EEG
  • Biometrics
  • Time Series Classification
  • Deep Learning
  • Authentication

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