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    Area of Science:

    • Neuroscience and Biomedical Engineering
    • Brain-Computer Interfaces (BCI)
    • Machine Learning in Healthcare

    Background:

    • Brain-computer interface (BCI) platforms enable device control via neural signals, bypassing motor pathways.
    • Traditional EEG-based cursor control relies on sensorimotor rhythms, often requiring extensive training.
    • Imagined body kinematics (IBK) offers a novel approach for more intuitive and rapid BCI cursor control.

    Purpose of the Study:

    • To investigate and identify optimal decoding algorithms for the IBK paradigm using electroencephalography (EEG) signals.
    • To enhance neural cursor control by improving the efficiency and accuracy of IBK-based BCI systems.
    • To analyze the contribution of individual EEG channels and frequency bands to cursor movement prediction.

    Main Methods:

    • Offline analysis of EEG data from 32 healthy subjects performing IBK cursor control tasks.
    • Implementation and comparison of various machine learning techniques, including linear regression least squares and Theil-Sen regressor, for predicting cursor kinematics.
    • Development of a directional classifier utilizing EEG features from specific frequency bands to distinguish horizontal and vertical cursor movements.

    Main Results:

    • Linear regression least squares models achieved the highest goodness-of-fit: 70% for horizontal and 40% for vertical cursor prediction.
    • Analysis revealed the specific contributions of individual EEG channels to horizontal and vertical cursor kinematics prediction.
    • The directional classifier achieved 80% accuracy in differentiating horizontal versus vertical cursor movements by incorporating frequency band features.

    Conclusions:

    • Optimal decoding algorithms, particularly linear regression, can significantly improve IBK-based neural cursor control.
    • Understanding EEG channel contributions and utilizing frequency-specific features enhances directional classification accuracy.
    • These findings provide a foundation for developing more efficient and effective online IBK-BCI systems for neural cursor control.