Prototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces

Serkan Musellim, Dong Kyun Han, Ji Hoon Jeong, Seong Whan Lee

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

    9 Citations (Scopus)

    Abstract

    Brain-computer interface (BCI) is challenging to use in practice due to the inter/intra-subject variability of electroencephalography (EEG). The BCI system, in general, necessitates a calibration technique to obtain subject/session-specific data in order to tune the model each time the system is utilized. This issue is acknowledged as a key hindrance to BCI, and a new strategy based on domain generalization has recently evolved to address it. In light of this, we've concentrated on developing an EEG classification framework that can be applied directly to data from unknown domains (i.e. subjects), using only data acquired from separate subjects previously. For this purpose, in this paper, we proposed a framework that employs the open-set recognition technique as an auxiliary task to learn subject-specific style features from the source dataset while helping the shared feature extractor with mapping the features of the unseen target dataset as a new unseen domain. Our aim is to impose cross-instance style in-variance in the same domain and reduce the open space risk on the potential unseen subject in order to improve the generalization ability of the shared feature extractor. Our experiments showed that using the domain information as an auxiliary network increases the generalization performance. Clinical relevance - This study suggests a strategy to improve the performance of the subject-independent BCI systems. Our framework can help to reduce the need for further calibration and can be utilized for a range of mental state monitoring tasks (e.g. neurofeedback, identification of epileptic seizures, and sleep disorders).

    Original languageEnglish
    Title of host publication44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages711-714
    Number of pages4
    ISBN (Electronic)9781728127828
    DOIs
    Publication statusPublished - 2022
    Event44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022 - Glasgow, United Kingdom
    Duration: 2022 Jul 112022 Jul 15

    Publication series

    NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
    Volume2022-July
    ISSN (Print)1557-170X

    Conference

    Conference44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
    Country/TerritoryUnited Kingdom
    CityGlasgow
    Period22/7/1122/7/15

    Bibliographical note

    Publisher Copyright:
    © 2022 IEEE.

    ASJC Scopus subject areas

    • Signal Processing
    • Biomedical Engineering
    • Computer Vision and Pattern Recognition
    • Health Informatics

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