Abstract
Upper limb movement classification, which maps input signals to the target activities, is one of the crucial areas in the control of rehabilitative robotics. Classifiers are trained for the rehabilitative system to comprehend the desires of the patient whose upper limbs do not function properly. Electromyography (EMG) signals and Electroencephalography (EEG) signals are used widely for upper limb movement classification. By analysing the classification results of the real-time EEG and EMG signals, the system can understand the intention of the user and predict the events that one would like to carry out. Accordingly, it will provide external help to the user to assist one to perform the activities. However, not all users process effective EEG and EMG signals due to the noisy environment. The noise in the real-time data collection process contaminates the effectiveness of the data. Moreover, not all patients process strong EMG signals due to muscle damage and neuromuscular disorder. To address these issues, we would like to propose a novel decision-level multisensor fusion technique. In short, the system will integrate EEG signals with EMG signals, retrieve effective information from both sources to understand and predict the desire of the user, and thus provide assistance. By testing out the proposed technique on a publicly available WAY-EEG-GAL dataset, which contains EEG and EMG signals that were recorded simultaneously, we manage to conclude the feasibility and effectiveness of the novel system.
Abstract (translated)
URL
https://arxiv.org/abs/2207.08650
