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FORCe: Fully Online and Automated Artifact Removal for Brain-Computer Interfacing.

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    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |August 19, 2014
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    Summary

    A new automated method called FORCe effectively removes artifacts from electroencephalogram (EEG) signals during brain-computer interfacing (BCI). This advanced technique improves EEG data quality for BCI applications.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Artifacts in electroencephalogram (EEG) signals pose a significant challenge for brain-computer interfacing (BCI).
    • Existing automated artifact removal methods often require extensive computational resources or additional physiological signals.
    • Effective artifact removal is crucial for reliable BCI performance, especially for individuals with neurological conditions like cerebral palsy (CP).

    Purpose of the Study:

    • To develop a fully automated and online artifact removal method for EEG signals specifically designed for BCI applications.
    • To introduce the FORCe (Fully automated artifact removal) method, which combines wavelet decomposition, independent component analysis, and thresholding.
    • To evaluate the performance of FORCe against existing state-of-the-art methods and assess its ability to remove various artifact types.

    Main Methods:

    • The FORCe method utilizes a novel combination of wavelet decomposition, independent component analysis, and thresholding for artifact detection and removal.
    • The method is designed to operate online during EEG acquisition using a minimal set of EEG channels.
    • FORCe does not require supplementary signals such as electrooculogram (EOG) recordings.

    Main Results:

    • Offline evaluation on EEG data from 13 BCI participants with cerebral palsy (CP) demonstrated FORCe's effectiveness.
    • Online evaluation with three healthy participants confirmed the method's real-time applicability.
    • FORCe significantly outperformed established automated artifact removal techniques, including Lagged Auto-Mutual Information Clustering (LAMIC) and Fully Automated Statistical Thresholding for EEG artifact Rejection (FASTER).
    • The method successfully removed diverse artifacts, including blink, electromyogram (EMG), and electrooculogram (EOG) artifacts.

    Conclusions:

    • The developed FORCe method offers a robust and efficient solution for automated online artifact removal in EEG for BCI.
    • FORCe's ability to operate effectively with a small channel set and without auxiliary signals makes it highly practical for real-world BCI implementations.
    • This advancement holds significant potential for improving the usability and reliability of BCI systems, particularly for individuals with CP.