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Comparison of MI-EEG Decoding in Source to Sensor Domain.

Tao Fang, Zuoting Song, Wei Mu

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |September 9, 2022
    PubMed
    Summary

    This study introduces a novel brain-computer interface (BCI) framework for motor imagery electroencephalography (MI-EEG) classification. Performing decoding in the source domain, rather than the sensor domain, significantly improves classification accuracy and effective channel count.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Signal Processing

    Background:

    • Sensorimotor rhythm (SMR)-based brain-computer interface (BCI) systems offer natural human-computer interaction.
    • Motor imagery electroencephalography (MI-EEG) classification is crucial for BCI development.
    • Traditional EEG decoding faces challenges due to volume conduction effects in the sensor domain.

    Purpose of the Study:

    • To propose a novel multi-task MI-EEG classification framework.
    • To perform EEG decoding in the source domain to overcome sensor domain limitations.
    • To enhance the accuracy and efficiency of BCI systems.

    Main Methods:

    • Constructed a signal conduction model using the ICBM152 head model and boundary element method (BEM).
    • Mapped sensor-domain EEG to the cortex using standardized low-resolution electromagnetic tomography (sLORETA) to address volume conduction.
    • Extracted and classified source-domain features using FBCSP and Linear Discriminant Analysis (LDA).

    Main Results:

    • The source-domain decoding algorithm effectively solved the MI-EEG classification task.
    • Source imaging significantly increased the number of available EEG channels, at least doubling the count.
    • Demonstrated superior performance of source-domain decoding compared to sensor-domain decoding.

    Conclusions:

    • Performing MI-EEG decoding in the source domain yields better results than in the sensor domain.
    • The proposed framework enhances BCI performance by leveraging source imaging techniques.
    • Encourages further implementation of source-domain EEG decoding algorithms for advanced BCI applications.