Bio-signal based motion control system using deep learning models: a deep learning approach for motion classification using EEG and EMG signal fusion

Abstract

Bioelectrical time signals are the signals that can be measured through the electrical potential difference across an organ over the time. Electroencephalography (EEG) signals and Electromyography (EMG) signals are among the best-known bioelectrical signals used for medical diagnosis and motion classification. In traditional machine learning methods, the task of extracting unique patterns and features from both bioelectrical signals is hard and requires a specific expert knowledge to study the non-linear, non-stationary and complex nature of these signals. With recent advancement in deep learning methods, features can be extracted from raw data without any handcrafted features. In this paper, a new deep learning approach that integrates EEG with EMG signals is proposed to investigate the efficiency of deep learning in hybrid systems with signal fusion and study the effect of hyper parameters tuning to enhance classification accuracy and boost the performance of hand and wrist motion control without manual feature engineering. Three deep learning models including Convolution Neural Networks CNN model, Long Short Term recurrent neural networks model LSTM, and a combined CNN–LSTM model, were proposed for hybrid system for signal classification. Experiments were tested on a dataset of multi-channel EEG signals merged with multi-channel sEMG signals, to decode hand and wrist motion. Experimental results signify that the proposed deep learning models achieve high classification accuracies that outperform other traditional machine learning state-of-the-art methods with up to 3.5% improvement ratio which indicates the promising application of the approach. Consequently, this work contributes to an automatic classification that facilitates and improves the real-time control of bio-robotics applications, mainly for limb movement classification.