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Kmeans-ICA based automatic method for ocular artifacts removal in a motorimagery classification.

Elie Bou Assi, Sandy Rihana, Mohamad Sawan

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 9, 2015
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    Summary

    This study introduces a novel method for removing eye blink artifacts from electroencephalogram (EEG) signals in brain-computer interface (BCI) systems. The approach uses K-means clustering for artifact identification, improving motor imagery classification accuracy.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Electroencephalogram (EEG) signals are crucial for brain-computer interface (BCI) systems, particularly for motor imagery tasks.
    • Ocular artifacts, such as eye blinks, significantly contaminate EEG signals, overlapping with the frequency bands of interest.
    • Existing artifact removal methods often rely on reference ElectroOculoGram (EOG) leads, which may not always be available or practical.

    Purpose of the Study:

    • To develop and evaluate an artifact removal technique for EEG signals in motor imagery BCI systems.
    • To identify and remove ocular artifacts without the need for external reference leads like EOG.
    • To improve the accuracy of motor imagery classification by utilizing denoised EEG data.

    Main Methods:

    • Independent Component Analysis (ICA) was employed for artifact separation.
    • K-means clustering with adaptive thresholding was utilized to automatically identify artifactual components.
    • Feature extraction included band power, coherence, and phase locking value.
    • A linear discriminant analysis classifier was used for motor imagery classification.

    Main Results:

    • The proposed method successfully identified and removed ocular artifacts from EEG signals.
    • Denoised EEG data led to improved performance in the motor imagery classification task.
    • The artifact removal technique did not require EOG reference signals, offering a more flexible approach.

    Conclusions:

    • The K-means clustering-based artifact identification method is effective for removing eye blinks from EEG signals in BCI applications.
    • This approach enhances the robustness and accuracy of motor imagery classification.
    • The study presents a valuable alternative for artifact removal in BCI systems where EOG is not used.