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    Area of Science:

    • Neuroscience
    • Human-Computer Interaction
    • Robotics

    Background:

    • Brain-computer interface (BCI) systems, primarily using electroencephalography (EEG), show promise for individuals with motor paralysis.
    • There is a growing interest in adapting BCI technology for healthy users, focusing on human factors like user training.
    • Current BCI training often relies on abstract visual feedback, with limited investigation into optimizing user-generated EEG patterns.

    Purpose of the Study:

    • To investigate a novel BCI training protocol utilizing a human-like android robot (Geminoid HI-2) for realistic visual feedback.
    • To address limitations of classical BCI training methods by leveraging body-abled user capabilities.
    • To enhance the modulation of sensorimotor rhythms crucial for motor imagery-based BCI control.

    Main Methods:

    • Implemented a BCI training protocol incorporating realistic visual feedback from the Geminoid HI-2 android robot.
    • Compared the effectiveness of android feedback training against classical abstract feedback methods.
    • Analyzed EEG signals to assess sensorimotor rhythm modulation during motor imagery tasks.

    Main Results:

    • Android feedback-based BCI training demonstrated improved modulation of sensorimotor rhythms during motor imagery.
    • The study suggests that realistic feedback enhances the user's ability to generate high-quality EEG patterns.
    • Explored the potential influence of body ownership transfer illusion toward the android on brain activity.

    Conclusions:

    • A novel BCI training protocol using android feedback can enhance sensorimotor rhythm modulation for motor imagery tasks.
    • Realistic visual feedback, potentially enhanced by body ownership illusions, may be a key factor in optimizing BCI user training.
    • This approach offers a promising direction for developing more effective BCI applications for both clinical and healthy users.