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    This study introduces a novel algorithm for analyzing electroencephalography (EEG) signals during motor imagery (MI) tasks. The new method significantly enhances accuracy and reduces computational load for brain-computer interface (BCI) applications.

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    Area of Science:

    • Neuroscience and Biomedical Engineering
    • Brain-Computer Interface (BCI) Technology
    • Signal Processing and Machine Learning

    Background:

    • Electroencephalography (EEG) signal analysis for motor imagery (MI) is complex due to numerous channels and features.
    • Existing brain-computer interface (BCI) applications face challenges in efficiently processing high-dimensional EEG data.
    • The combinatorial search complexity in MI analysis hinders the development of practical BCI systems.

    Purpose of the Study:

    • To develop an efficient algorithm for EEG-based motor imagery (MI) signal analysis.
    • To reduce the computational complexity and improve the accuracy of BCI systems.
    • To identify optimal channels and a minimal set of features for enhanced MI detection.

    Main Methods:

    • A two-step multiobjective set-based integer-coded fuzzy-initialized evolutionary algorithm (MOSIFE) was proposed.
    • A non-dominant wrapper strategy was employed for sequential channel and feature selection.
    • A reptile search algorithm (RSA) was utilized for optimizing classifier hyperparameters.

    Main Results:

    • The MOSIFE-RSA algorithm achieved a 20% improvement in accuracy compared to 12 benchmark algorithms.
    • Channel selection contributed up to 15% and feature selection up to 5% to the accuracy gains.
    • Computational complexity was reduced by 81% via channel selection and 16% via feature selection.

    Conclusions:

    • The proposed MOSIFE-RSA algorithm effectively addresses the complexities of EEG-based MI signal analysis.
    • This approach significantly enhances accuracy and reduces computational load in BCI applications.
    • The findings have practical implications for developing more efficient and accurate BCI systems.