Interactive and Deep Learning-Powered EEG-BCI for Wrist Rehabilitation: A Game-based Prototype Study

Motor deficits induced by neurological disorders impose a severe impact on activities of daily life. Conventional rehabilitation practices necessitate ongoing clinical supervision, which is costly and inaccessible. EEG-based brain-computer interface (BCI) systems offer a viable solution by facilitating neurorehabilitation through the direct interpretation of brain signals. Nonetheless, current systems are confronted with issues of real-time control, portability, and classification precision. The paper describes a novel EEG-controlled wrist rehabilitation robot with deep learning-based real-time motor intention classification. EEG signals were recorded with OpenBCI, preprocessed with noise filtering, and converted into time-frequency representations. A GoogLeNet-inspired convolutional neural network (CNN) was trained for the classification of wrist movement intentions. SolidWorks was utilized for designing the mechanical structure, which was verified using finite element analysis (FEA). An Nvidia-based microcontroller was employed for controlling servo motors, while an inertial measurement unit (IMU) was incorporated into the system for enabling precise and agile movement using feedback control. The system proposed in this work attained an EEG classification accuracy of 90.24%, which was well above conventional feature-based classifiers. The 2-degree-of-freedom (2-DoF) robotic system with a lightweight structure enabled controlled wrist flexion, extension, and radial/ulnar deviation movements. Structural validation by the FEA assured mechanical stability against operational loads. The system proved to be feasible for real-time, user-intended motion control. The proposed study offers a cost-effective, portable, and deep learning-based EEG-BCI rehabilitation robot, rendering a possible solution to neurorehabilitation. The high classification accuracy and real-time control features of the system highlight the potential for personalized rehabilitation. Future endeavors will focus on the development of deeper learning frameworks, the advancement of motor control strategies, and the implementation of extended clinical trials.