Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training

Thomas Bender, Troels W. Kjaer, Carsten E. Thomsen, Helge B.D. Sorensen, S. Puthusserypady

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    Abstrakt

    This paper presents a novel and computationally simple tri-training based semi-supervised steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). It is implemented with autocorrelation-based features and a Naïve-Bayes classifier (NBC). The system uses nine characters presented on a 100 Hz CRT-monitor, three scalp electrodes for signal acquisition, a gUSB-amp for preamplification and two PCs for data-processing and stimulus control respectively. Preliminary test results of the system on nine healthy subjects, with and without tri-training, indicates that the accuracy improves as a result of tri-training.

    OriginalsprogEngelsk
    Titel2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
    Sider4279-4282
    Antal sider4
    DOI
    StatusUdgivet - 31 okt. 2013
    Begivenhed2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 - Osaka, Japan
    Varighed: 3 jul. 20137 jul. 2013

    Publikationsserier

    NavnProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
    ISSN (Trykt)1557-170X

    Konference

    Konference2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
    LandJapan
    ByOsaka
    Periode3/07/137/07/13

    Fingeraftryk Udforsk hvilke forskningsemner 'Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training' indeholder.

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