  Upper limb movement classification, which maps input signals to the target
activities, is a key building block 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. However, the noise in the real-time
EEG and EMG data collection process contaminates the effectiveness of the data,
which undermines classification performance. Moreover, not all patients process
strong EMG signals due to muscle damage and neuromuscular disorder. To address
these issues, this paper explores different feature extraction techniques and
machine learning and deep learning models for EEG and EMG signals
classification and proposes a novel decision-level multisensor fusion technique
to integrate EEG signals with EMG signals. This system retrieves effective
information from both sources to understand and predict the desire of the user,
and thus aid. 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.
