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EEG Artifacts Handling in a Real Practical Brain-Computer Interface Controlled Vehicle.

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    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |May 17, 2019
    PubMed
    Summary
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    Physiological artifacts in electroencephalography (EEG) recordings significantly impair brain-computer interface (BCI) performance. Reducing these artifacts, like ocular and muscular interference, substantially enhances BCI efficiency for practical applications.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Brain-computer interfaces (BCIs) enable control via neural signals but are hindered by physiological artifacts in electroencephalography (EEG).
    • Common artifacts, such as ocular and muscular signals, possess high amplitude and frequency, significantly corrupting EEG data.
    • Current practical BCI systems often neglect artifact removal due to computational complexity and time constraints.

    Purpose of the Study:

    • To investigate the impact of physiological artifacts on practical BCI system performance.
    • To evaluate the effectiveness of artifact reduction techniques in improving BCI efficiency.
    • To demonstrate the practical application of BCI for controlling a radio-control (RC) car using motor imagery.

    Main Methods:

    • An experimental paradigm was designed using a BCI system to control an RC car.
    • Two distinct motor imagery tasks (left-hand and right-hand movement imagination) were employed as control commands.
    • Ocular and muscular artifacts were identified as the primary focus for analysis and mitigation.
    • The system's performance was assessed with and without artifact reduction strategies.

    Main Results:

    • Physiological artifacts were found to significantly degrade the performance of the BCI system.
    • Implementing artifact reduction methods led to a substantial improvement in the BCI system's efficiency.
    • The BCI-controlled RC car successfully navigated left and right turns based on motor imagery commands.

    Conclusions:

    • Artifacts pose a critical challenge to the real-world usability of BCI systems.
    • Effective artifact reduction is crucial for enhancing the reliability and performance of practical BCIs.
    • This study highlights the potential of artifact mitigation to unlock the full capabilities of BCI technology.