Hand Grasp Motion Intention Recognition Based on High-Density Electromyography in Chronic Stroke Patients.

Stroke is a debilitating condition that leads to a loss of motor function, inability to perform daily life activities, and ultimately worsening quality of life. Robot-based rehabilitation is a more effective method than conventional rehabilitation but needs to accurately recognize the patient's intention so that the robot can assist the patient's voluntary motion. This study focuses on recognizing hand grasp motion intention using high-density electromyography (HD-EMG) in patients with chronic stroke. The study was conducted with three chronic stroke patients and involved recording HD-EMG signals from the muscles involved in hand grasp motions. The adaptive onset detection algorithm was used to accurately identify the start of hand grasp motions accurately, and a convolutional neural network (CNN) was trained to classify the HD-EMG signals into one of four grasping motions. The average true positive and false positive rates of the grasp onset detection on three subjects were 91.6% and 9.8%, respectively, and the trained CNN classified the grasping motion with an average accuracy of 76.3%. The results showed that using HD-EMG can provide accurate hand grasp motion intention recognition in chronic stroke patients, highlighting the potential for effective robot-based rehabilitation.

EEG-EMG hybrid real-time classification of hand grasp and release movements intention in chronic stroke patients.

Rehabilitation of the hand motor function is essential for stroke patients to resume activities of daily living. Recent studies have shown that wearable robot systems, like a multi degree-of-freedom soft glove, have the potential to improve hand motor impairment. The rehabilitation system, which is intuitively controlled according to the user's intention, is expected to induce active participation of the user and further promote brain plasticity. However, due to the patient-specific nature of stroke patients, extracting the intention from stroke patients is still challenging. In this study, we implemented a classifier that combines EEG and EMG to detect chronic stroke patients' four types of intention: rest, grasp, hold, and release. Three chronic stroke patients participated in the experiment and performed rest, grasp, hold, and release actions. The rest vs. grasp binary classifier and release vs. hold binary classifier showed 76.9% and 86.6% classification accuracy in real-time, respectively. In addition, patient-specific accuracy comparisons showed that the hybrid approach was robust to upper limb impairment level compared to other approaches. We believe that these results could pave the way for the development of BCI-based robotic hand rehabilitation therapy.