Graph Neural Networks for HD EMG-based Movement Intention Recognition: An Initial Investigation

Recently, high-density (HD) EMG electrodes have been proposed for improving amputees' movement/grasping intention recognition, exploiting different machine learning techniques. HD EMG electrodes are composed of a large number of closely spaced channels that simultaneously acquire EMG signals from different parts of the muscle. Given the topological properties of these devices, it is important to fully exploit the spatiotemporal information provided by the electrodes to optimize recognition accuracy. In this work, we introduce the use of Graph Neural Networks (GNNs) to process HD EMG data for movement intention recognition of people with an amputation affecting the upper limbs and which use a robotic prosthesis. In this initial investigation of the approach, we conducted experiments using a real-world dataset consisting of EMG signals collected from 20 volunteers while performing 65 different gestures. We were able to detect 45 gestures with a classification error rate of less than 10%, and obtained an overall classification error rate of 8.75% with a standard deviation of 4.9. To the best of our knowledge, this is the first work in which GNNs are used for processing HD EMG data.