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High Density Event-related Potential Data Acquisition in Cognitive Neuroscience
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Minimum Noise Estimate filter: a Novel Automated Artifacts Removal method for Field Potentials.

Reza Foodeh, Abed Khorasani, Vahid Shalchyan

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |January 24, 2017
    PubMed
    Summary

    A new automated method, the minimum noise estimate (MNE) filter, effectively removes artifacts from brain signals for brain-computer interface (BCI) applications without prior data knowledge. This novel technique outperforms existing artifact removal methods.

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    Area of Science:

    • Neuroscience
    • Signal Processing
    • Biomedical Engineering

    Background:

    • Artifacts in multichannel field potential signals (e.g., EEG, ECoG) can degrade the quality of brain-computer interface (BCI) data.
    • Existing artifact removal techniques often require prior information or specific assumptions about the signals.

    Purpose of the Study:

    • To introduce a novel automated and unsupervised method for artifact removal from multichannel field potential signals.
    • To evaluate the performance of the proposed method in brain-computer interface (BCI) applications.

    Main Methods:

    • Development of the minimum noise estimate (MNE) filter, an iterative thresholding technique combined with Rayleigh quotient.
    • The MNE filter estimates and minimizes noise within the original signal without requiring prior information.
    • Application and evaluation of the MNE filter on electrocorticogram (ECoG) and electroencephalogram (EEG) datasets.

    Main Results:

    • The MNE filter demonstrated superior performance in artifact removal compared to established methods.
    • The method successfully processed both ECoG and EEG datasets.
    • Artifact removal using MNE filter improved signal quality for downstream decoding procedures.

    Conclusions:

    • The MNE filter is an effective, automated, and unsupervised approach for artifact removal in BCI applications.
    • The method's ability to operate without prior signal information makes it broadly applicable.
    • MNE filter offers a significant advancement over current artifact removal techniques for neural signal processing.