Deep Learning Based Recognition of Hand Movement Intention EEG in Patients with Spinal Cord Injury

Abstract

In the field of medicine and rehabilitation, brain-controlled prosthetic hands can help patients with spinal cord injury to carry out daily activities. However, the recognition of EEG signals aiming at the hand movement intention of disabled patients has been faced with the difficulties of low accuracy, poor stability and weak robustness. Especially for unilateral hand movement control, the EEG intention of distinguishing different movements comes from unilateral cerebral cortex, and the degree of confusion is high. In this paper, deep learning method is introduced for EEG signal perception and recognition. A convolutional neural network model is proposed, combining with Common Spatial Patterns, to identify the intention of unilateral hand movement motion (palm extension, hand grasp). EEG signals from 15 healthy subjects and 10 patients with spinal cord injury were collected. Among the 15 healthy subjects, the average recognition accuracy was 91.57%., among which the optimal accuracy achieved 95.83%. In 10 patients with spinal cord injury., the average recognition accuracy was 78.03%, among which the optimal subjects' recognition accuracy was 82.41%. In addition, an off-line EEG recognition and control system was set up. The average accuracy of a subject was 87.92%., and the average system recognition and control time was 13.9ms. With excellent accuracy and recognition speed, this method has great value and application prospect in the fields of braincomputer interface, robotics and rehabilitation medicine for the disabled.