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Bring mental activity into action! An enhanced online co-adaptive brain-computer interface training protocol.

Reinhold Scherer, Josef Faller, Eloy Opisso

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
    PubMed
    Summary

    This study introduces a new Brain-Computer Interface (BCI) training method that automatically adapts to users. It helps individuals with disabilities learn to control BCI technology more effectively, with promising results in early trials.

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    Area of Science:

    • Neuroscience and Biomedical Engineering
    • Brain-Computer Interface (BCI) Technology

    Background:

    • Non-stationarity and variability in electroencephalogram (EEG) signals complicate the recognition of spontaneous EEG patterns.
    • Effective Brain-Computer Interface (BCI) control requires users to learn reliable modulation of EEG patterns.
    • Existing BCI training methods may not be efficient for all users, especially those with disabilities.

    Purpose of the Study:

    • To present a novel online co-adaptive BCI training paradigm.
    • To autonomously screen users for their ability to modulate EEG patterns predictively.
    • To adapt BCI model parameters online for improved user performance.

    Main Methods:

    • Development of a fully automatic, co-adaptive online BCI training system.
    • Online screening of user's EEG modulation capabilities.
    • Real-time adaptation of BCI model parameters based on user performance.
    • Utilized a single bipolar EEG channel for online control.

    Main Results:

    • Three out of seven first-time BCI users with disabilities achieved over 70% accuracy in 2-class BCI control after 24 minutes of training.
    • Online performance was significantly above chance level for six out of seven users.
    • Beta band activity was identified as the most discriminative information for BCI control.

    Conclusions:

    • The developed co-adaptive online BCI approach is effective in training users with disabilities.
    • This method allows for rapid evaluation of user benefit from current BCI technology.
    • The system demonstrates potential for efficient and personalized BCI training.