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    This study introduces novel deep learning models for predicting hand movement trajectories from electroencephalography (EEG) signals, significantly improving upon existing methods for brain-computer interfaces (BCI) used with exoskeletons.

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    Area of Science:

    • Neuroscience
    • Robotics
    • Machine Learning

    Background:

    • Noninvasive electroencephalography (EEG) is crucial for controlling exoskeletons to augment human strength and endurance.
    • Conventional brain-computer interfaces (BCI) use discrete signals, while continuous kinematic reconstruction from EEG offers greater practical utility.
    • Current state-of-the-art multivariable linear regression (mLR) achieves a correlation of up to 0.67 for hand kinematics estimation.

    Purpose of the Study:

    • To develop and evaluate novel deep learning models for continuous motion trajectory prediction (MTP) from EEG signals.
    • To enhance BCI performance by integrating brain source localization (BSL) for improved motor intention decoding.
    • To enable more precise and real-time control of assistive robotic devices.

    Main Methods:

    • Proposed three novel source-aware deep learning models: multilayer perceptron (MLP), convolutional neural network-long short-term memory (CNN-LSTM), and wavelet packet decomposition (WPD) enhanced CNN-LSTM.
    • Utilized brain source localization (BSL) with standardized low-resolution brain electromagnetic tomography (sLORETA) for channel and EEG time segment selection.
    • Compared model performance against the traditional mLR method using the reach, grasp, and lift (GAL) dataset.

    Main Results:

    • The proposed deep learning models demonstrated significant improvement in correlation coefficients compared to the state-of-the-art mLR method.
    • Pearson correlation coefficient (PCC) and trajectory analysis confirmed the enhanced accuracy of the novel framework.
    • The integration of BSL improved the reliability of motor intention decoding and EEG signal selection.

    Conclusions:

    • Novel deep learning models, particularly CNN-LSTM with WPD and BSL, offer superior performance for EEG-based kinematic reconstruction.
    • The developed framework effectively bridges the gap between BCI control and actuator systems, paving the way for real-time applications.
    • This research advances the development of sophisticated BCI systems for exoskeleton and exosuit control.