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    Simaretal.2020IEEEN-21254-1.pdf (1.152Mo)
    Date
    2020-09-28
    Auteur
    Simar, Cédric
    Petieau, Mathieu
    Cebolla, Anita
    Leroy, Axelle
    Bontempi, Gianluca
    Cheron, Guy
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    EEG-based brain-computer interface for alpha speed control of a small robot using the MUSE headband

    Résumé
    Non-invasive BMI applications are increasingly used in different contexts ranging from industrial, clinical and gaming. After having tested the difference between a classical EEG recorder with electroconductive gel (ANT system) and the MUSE EEG headband, we studied the BCI performances of the later during the control of a small robot. We demonstrated that the participants were able to successfully control the robot using an online brain-computer interface based on the signal power in different frequency bands (delta, theta and alpha) characterizing the eyes-opened and relaxed eyes-closed states. Additionally, we performed a correlation analysis which demonstrated that the BCI commands were more related to a delta or theta power decrease for the determination of the classifier output probability and to the alpha power increase for the speed control of the robot.

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