A novel deep learning approach to predict subject arm movements from EEG-based signals

Sachin Kansal, Dhruv Garg, Aditya Upadhyay, Snehil Mittal, Guneet Singh Talwar

Abstract: Around 3 million people worldwide have an arm amputation. These people face a lot of trouble in their everyday life whilst performing basic tasks. This paper proposes a novel deep learning-based approach for predicting arm movements using EEG-based signals. We plan to design and develop an active exoskeleton controlled by the same EEG-based signals to rehabilitate the amputees. The architecture design is intended to build an exoskeleton arm with at least 3 degrees of freedom that can perform complex movements and is sophisticated enough to substitute for a real arm. This prosthetic arm will be controlled using electroencephalogram (EEG) signals gathered by different devices/headsets and processed using deep learning models. The results show that our proposed approach gives excellent results.