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Unsupervised Eye Blink Artifact Detection From EEG With Gaussian Mixture Model.

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    IEEE Journal of Biomedical and Health Informatics
    |February 9, 2021
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    This study introduces an unsupervised algorithm for accurate electroencephalogram (EEG) eye blink detection. The novel method improves performance in applications like epilepsy recognition by effectively filtering artifacts.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Eye blinks are common artifacts in electroencephalogram (EEG) recordings.
    • These artifacts significantly degrade the performance of EEG-based diagnostic applications, including epilepsy recognition and encephalitis diagnosis.
    • Accurate and efficient eye blink detection is crucial for reliable EEG analysis.

    Purpose of the Study:

    • To develop a novel unsupervised learning algorithm for precise and efficient eye blink detection in EEG signals.
    • To improve the accuracy of EEG-related applications by effectively removing eye blink artifacts.
    • To evaluate the algorithm's performance on clinical EEG datasets.

    Main Methods:

    • A hybrid thresholding method is employed for preliminary screening of EEG signals based on amplitude, displacement, and cross-channel correlation.
    • Key features such as frontal electrode correlation (FP1, FP2), fractal dimension, and amplitude difference are extracted.
    • A Gaussian mixture model (GMM) is trained on these features for unsupervised eye blink detection.

    Main Results:

    • The proposed algorithm demonstrated superior performance in eye blink detection.
    • Achieved the highest detection precision and F1 score compared to existing state-of-the-art methods.
    • Validated on two large EEG datasets from Temple University Hospital and Children's Hospital, Zhejiang University School of Medicine.

    Conclusions:

    • The novel unsupervised algorithm provides an accurate and efficient solution for eye blink artifact removal in EEG.
    • This method enhances the reliability of EEG analysis for clinical applications, particularly in epilepsy and encephalitis diagnosis.
    • The GMM-based approach offers a robust and effective strategy for artifact detection in complex biological signals.