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    Improvements of a Brain-Computer Interface Applied to a Robotic Wheelchair

    TitleImprovements of a Brain-Computer Interface Applied to a Robotic Wheelchair
    Publication TypeBook Chapter
    Año de publicación2010
    AutoresFerreira, A, Freire, T, Sarcinelli, M, Martin, JL, Garcia, JC, Mazo, M
    Refereed DesignationUnknown
    Book TitleBiomedical Engineering Systems and Technologies
    Series TitleCommunications in Computer and Information Science
    ChapterImprovements of a Brain-Computer Interface Applied to a Robotic Wheelchair
    EdiciónAna Fred, Joaquim Filipe and Hugo Gamboa
    Fecha de publicación03/2010
    EditorialSpringer Berlin Heidelberg
    Idioma de publicaciónEnglish
    ISSN1865-0929 (Print), 1865-0937 (Online)
    Numero ISBN978-3-642-11720-6 (Print), 978-3-642-11721-3 (Online)
    Palabras claveBrain-Computer Interfaces, Power Spectral Density components, RoboticWheelchair., Support-Vector Machines

    Two distinct signal features suitable to be used as input to a Support-Vector Machine (SVM) classifier in an application involving hands motor imagery and the correspondent EEG signal are evaluated in this paper. Such features are the Power Spectral Density (PSD) components and the Adaptive Autoregressive (AAR) parameters. The best result (an accuracy of 97.1%) is obtained when using PSD components, while the AAR parameters generated an accuracy of 91.4%. The results also demonstrate that it is possible to use only two EEG channels (bipolar configuration around C_3 and C_4), discarding the bipolar configuration around C_z. The algorithms were tested with a proprietary EEG data set involving 4 individuals and with a data set provided by the University of Graz (Austria) as well. The resulting classification system is now being implemented in a Brain-Computer Interface (BCI) used to guide a robotic wheelchair.


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