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This research explored the integration of the real-time machine learning classification of motor imagery data with a brain-machine interface, leveraging prefabricated exoskeletons and an EEG headset integrated with virtual reality (VR). By combining these technologies, the study aimed to develop practical and scalable therapeutic applications for rehabilitation and daily motor training. The project showcased an optimized system designed to assess and train cognitive-motor functions in elderly individuals. Key innovations included a motor imagery EEG acquisition protocol for data classification and a machine learning framework leveraging deep learning with a wavelet packet transform for feature extraction. Comparative analyses were conducted with traditional models such as Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks. The performance was further enhanced through a random hyperparameter search, optimizing feature extraction and learning parameters to achieve high classification accuracy (89.23%). A novel VR fishing game was developed to dynamically respond to EEG outputs, enabling the performance of interactive motor imagery tasks in coordination with upper limb exoskeleton arms. While clinical testing is ongoing, the system demonstrates potential for increasing ERD/ERS polarization rates in alpha and beta waves among elderly users after several weeks of training. This integrated approach offers a tangible step forward in creating effective, user-friendly solutions for motor function rehabilitation.
Luna et al. (Sat,) studied this question.