EEG sensor driven assistive device for elbow and finger rehabilitation using deep learning

Abstract

In today's world, a large number of people suffer from motor impairment-related challenges. Rehabilitation is the main method used to overcome these difficulties. The goal of the paper is to develop a deep learning-based electroencephalogram (EEG) sensor-controlled assistive device for the rehabilitation of elbow and finger movements. We have introduced an innovative finger and elbow movement rehabilitation method using an EEG sensor. The EEG sensor's recorded EEG signals, attention values, and meditation values have been used for this purpose. This rehabilitation technique helps a person perform basic finger movement rehabilitation motions, such as finger extension and flexion. Also, basic elbow movement rehabilitation exercises, i.e., elbow extension and elbow flexion, can be performed by using this rehabilitation technique. In this research, an EEG sensor records the prefrontal lobe’s EEG signals, attention value, and meditation value of a person while the person performs motor imagery. A deep learning-based CNN-TLSTM (Convolution Neural Network-tanh Long Short-Term Memory) model with attention mechanism has been designed for decoding the EEG sensor recorded data. The trained deep learning model decides the course of action of the rehabilitation device. The designed model achieves an accuracy of 99.6%. A working prototype model of the rehabilitation device has been developed, and the overall success rate of the model is found to be 98.66%. The novelty of the paper lies in i) designing an attention-based CNN-TLSTM model for motor imagery classification and ii) developing a low-cost EEG sensor-driven rehabilitation device for finger and elbow movement rehabilitation.