Improving BCI Performance after Classification

D. Plass - Oude Bos, Hayrettin Gürkök, B. Reuderink, Mannes Poel

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    3 Citations (Scopus)
    44 Downloads (Pure)


    Brain-computer interfaces offer a valuable input modality, which unfortunately comes also with a high degree of uncertainty. There are simple methods to improve detection accuracy after the incoming brain activity has already been classified, which can be divided into (1) gathering additional evidence from other sources of information, and (2) transforming the unstable classification results to be more easy to control. The methods described are easy to implement, but it is essential to apply them in the right way. This paper provides an overview of the different techniques, showing where to apply them and comparing the effects. Detection accuracy is important, but there are trade-offs to consider. Future research should investigate the effectiveness of these methods in their context of use, as well as the optimal settings to obtain the right balance between functionality and meeting the user's expectations for maximum acceptance.
    Original languageUndefined
    Title of host publicationProceedings of the 14th ACM international conference on Multimodal interaction, ICMI 2012
    Place of PublicationNew York
    PublisherAssociation for Computing Machinery (ACM)
    Number of pages8
    ISBN (Print)978-1-4503-1467-1
    Publication statusPublished - Oct 2012
    Event14th International Conference on Multimodal Interaction, ICMI 2012 - Santa Monica, United States
    Duration: 22 Oct 201226 Oct 2012
    Conference number: 14

    Publication series



    Conference14th International Conference on Multimodal Interaction, ICMI 2012
    Abbreviated titleICMI
    Country/TerritoryUnited States
    CitySanta Monica


    • IR-83667
    • debouncing
    • macro
    • predictive model
    • smoothing
    • EWI-22918
    • Brain-Computer Interfaces
    • Context
    • Hysteresis
    • Multimodal
    • dwell time
    • Post processing
    • METIS-296231
    • HMI-HF: Human Factors

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