Classification of Task Weight During Dynamic Motion Using EEG–EMG Fusion

Abstract

Musculoskeletal disorders are the biggest cause of disability worldwide and wearable mechatronic rehabilitation devices have been proposed as a potential tool for providing treatment; however, before such devices can be widely adopted, improvements in reliability are necessary. Changes in system dynamics caused by user actions, such as picking up a weight, can greatly affect control stability; hence, detecting these changes can lead to improved system performance. It is difficult to integrate conventional sensing technologies for completing this task into a wearable device in an unobtrusive way, therefore an alternative solution using bioelectrical signals, such as electroencephalography (EEG) and electromyography (EMG), to detect task weight is proposed. In this study, EEG and EMG signals were collected during dynamic elbow flexion–extension motion at different speeds, while holding different weights. These biosignals were used to develop different EEG–EMG fusion models to classify the weight the user was holding while moving. It was found that using a Weighted Average fusion method, and incorporating speed information into the model, provided the best performance, with an accuracy of 83.01 ± 6.04% when classifying three task weights. This work demonstrated the feasibility of using EEG–EMG fusion for classification of task weight during dynamic motion, which can be used to improve the adaptability and robustness of wearable mechatronic rehabilitation devices.