Online Hand Movement Recognition System with EEG-EMG Fusion Using One-Dimensional Convolutional Neural Network

Upper limb amputees face significant challenges in their daily lives due to the loss of hand or arm functionality. Researchers have developed upper limb prostheses to restore normal hand movements for them. Most hand movement recognition systems of prostheses use electromyography (EMG) as the input signal source, but ignore the interrelationship with electroencephalography (EEG), which may contain valuable movement-related information as well. In order to enhance the accuracy of hand movement classification, we proposed a hand movement recognition system based on a one-dimensional convolutional neural network (1D-CNN) that combines EEG and EMG as the input signal sources to increase the quantity of accessible information. In this work, we collected the EEG and EMG of five subjects during the hand movements and used a 1D-CNN based model to classify the preprocessed signals. The average accuracy of using EEG-EMG fusion is 96.59±2.63%, significantly higher than 74.99±8.24% of using single EEG and 90.31±7.16% of using single EMG. Then, we applied the model trained by offline experiment for online recognition, and controlled the Pepper robot to complete the corresponding hand movements. The average accuracy of online recognition can reach 93.00±4.85% by using majority voting method. The results indicate that the method of EEG-EMG fusion can effectively enhance the performance of hand movement recognition system, which promote the development of upper limb prostheses and contribute to the rehabilitation of upper limb amputees.