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Efficient Artifact Removal from Low-Density Wearable EEG using Artifacts Subspace Reconstruction.

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    Artifacts Subspace Reconstruction (ASR) effectively removes artifacts in low-density mobile EEG systems. This method enhances Steady-State Visually Evoked Potential (SSVEP) responses by up to 45% in real-world brain-computer interface applications.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Low-density mobile EEG systems offer convenient brain activity monitoring outside labs.
    • These systems are prone to signal artifacts due to hardware and environmental constraints.
    • Artifacts Subspace Reconstruction (ASR) is a potential solution for artifact removal in EEG data.

    Purpose of the Study:

    • To evaluate the effectiveness of ASR on low-density EEG systems.
    • To analyze ASR performance using the BioWolf ultra-low-power, eight-channel system.
    • To assess ASR's impact on Steady-State Visually Evoked Potential (SSVEP) responses.

    Main Methods:

    • Acquired clean and artifact-contaminated EEG datasets using the BioWolf system from six adult participants during SSVEP sessions.
    • Applied Artifacts Subspace Reconstruction (ASR) to the low-density EEG data.
    • Optimized ASR parameters on a single-subject basis.

    Main Results:

    • ASR demonstrated efficient artifact correction even with a low-density, eight-channel system.
    • An overall enhancement of up to 40% in SSVEP response was observed after ASR application.
    • Optimizing ASR parameters per subject further increased SSVEP response by over 45%.

    Conclusions:

    • ASR is a viable and robust method for automatic artifact correction in low-density BCI systems.
    • ASR facilitates reliable brain activity monitoring in real-life scenarios using portable EEG.
    • The findings support the use of ASR for online artifact removal in mobile BCI applications.