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)
    32 Downloads (Pure)

    Abstract

    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)
    Pages587-594
    Number of pages8
    ISBN (Print)978-1-4503-1467-1
    DOIs
    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

    Name
    PublisherACM

    Conference

    Conference14th International Conference on Multimodal Interaction, ICMI 2012
    Abbreviated titleICMI
    CountryUnited States
    CitySanta Monica
    Period22/10/1226/10/12

    Keywords

    • HMI-MI: MULTIMODAL INTERACTIONS
    • 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

    Cite this

    Plass - Oude Bos, D., Gürkök, H., Reuderink, B., & Poel, M. (2012). Improving BCI Performance after Classification. In Proceedings of the 14th ACM international conference on Multimodal interaction, ICMI 2012 (pp. 587-594). New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/2388676.2388799
    Plass - Oude Bos, D. ; Gürkök, Hayrettin ; Reuderink, B. ; Poel, Mannes. / Improving BCI Performance after Classification. Proceedings of the 14th ACM international conference on Multimodal interaction, ICMI 2012. New York : Association for Computing Machinery (ACM), 2012. pp. 587-594
    @inproceedings{91332bcb7f4e4ef3b9a9106b6af57af6,
    title = "Improving BCI Performance after Classification",
    abstract = "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.",
    keywords = "HMI-MI: MULTIMODAL INTERACTIONS, 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",
    author = "{Plass - Oude Bos}, D. and Hayrettin G{\"u}rk{\"o}k and B. Reuderink and Mannes Poel",
    note = "10.1145/2388676.2388799",
    year = "2012",
    month = "10",
    doi = "10.1145/2388676.2388799",
    language = "Undefined",
    isbn = "978-1-4503-1467-1",
    publisher = "Association for Computing Machinery (ACM)",
    pages = "587--594",
    booktitle = "Proceedings of the 14th ACM international conference on Multimodal interaction, ICMI 2012",
    address = "United States",

    }

    Plass - Oude Bos, D, Gürkök, H, Reuderink, B & Poel, M 2012, Improving BCI Performance after Classification. in Proceedings of the 14th ACM international conference on Multimodal interaction, ICMI 2012. Association for Computing Machinery (ACM), New York, pp. 587-594, 14th International Conference on Multimodal Interaction, ICMI 2012, Santa Monica, United States, 22/10/12. https://doi.org/10.1145/2388676.2388799

    Improving BCI Performance after Classification. / Plass - Oude Bos, D.; Gürkök, Hayrettin; Reuderink, B.; Poel, Mannes.

    Proceedings of the 14th ACM international conference on Multimodal interaction, ICMI 2012. New York : Association for Computing Machinery (ACM), 2012. p. 587-594.

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

    TY - GEN

    T1 - Improving BCI Performance after Classification

    AU - Plass - Oude Bos, D.

    AU - Gürkök, Hayrettin

    AU - Reuderink, B.

    AU - Poel, Mannes

    N1 - 10.1145/2388676.2388799

    PY - 2012/10

    Y1 - 2012/10

    N2 - 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.

    AB - 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.

    KW - HMI-MI: MULTIMODAL INTERACTIONS

    KW - IR-83667

    KW - debouncing

    KW - macro

    KW - predictive model

    KW - smoothing

    KW - EWI-22918

    KW - Brain-Computer Interfaces

    KW - Context

    KW - Hysteresis

    KW - Multimodal

    KW - dwell time

    KW - Post processing

    KW - METIS-296231

    KW - HMI-HF: Human Factors

    U2 - 10.1145/2388676.2388799

    DO - 10.1145/2388676.2388799

    M3 - Conference contribution

    SN - 978-1-4503-1467-1

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    BT - Proceedings of the 14th ACM international conference on Multimodal interaction, ICMI 2012

    PB - Association for Computing Machinery (ACM)

    CY - New York

    ER -

    Plass - Oude Bos D, Gürkök H, Reuderink B, Poel M. Improving BCI Performance after Classification. In Proceedings of the 14th ACM international conference on Multimodal interaction, ICMI 2012. New York: Association for Computing Machinery (ACM). 2012. p. 587-594 https://doi.org/10.1145/2388676.2388799