Upper-limb amputation imposes significant burden on amputees thereby restricting them from fully exploring their environments during activities of daily living. The use of intelligent learning algorithm for electromyogram-pattern recognition (EMG-PR)-based control in upper-limb prostheses is considered as an important clinical option. Though the existing EMG-PR prostheses could discriminate multiple degrees of freedom (DOF) limb movements, their transition to clinically viable option is still being challenged by some confounding factors. Toward realizing a clinically viable multiple DOF prostheses, this paper first explored the principles and dynamics of the existing intelligently driven EMG-PR-based prostheses control scheme. Then, investigations on core issues including variation in muscle contraction force, electrode shift, and subject mobility affecting the existing EMG-PR prosthetic control scheme were reported. For instance, variation in muscle contraction force and subject mobility led to degradation in the performance of the EMG-PR controlled prostheses with approximately 17.00% and 8.98% error values, respectively, which are still challenging issues among others. Thus, this paper reports core issues and best practices with respect to intelligent EMG-PR controlled prosthesis, the major challenges in implementing adaptively robust control scheme and provides future research directions that may result in the clinical realization of intuitively dexterous multiple DOF EMG-PR-based prostheses in the near future.

In signal processing, multiresolution decomposition techniques allow for the separation of an acquired signal into sub levels, where the optimal level within the signal minimises redundancy, uncertainties, and contains the information required for the characterisation of the sensed phenomena. In the area of physiological signal processing for prosthesis control, scenarios where a signal decomposition analysis are required: the wavelet decomposition (WD) has been seen to be the favoured time-frequency approach for the decomposition of non-stationary signals. From a research perspective, the WD in certain cases has allowed for a more accurate motion intent decoding process following feature extraction and classification. Despite this, there is yet to be a widespread adaptation of the WD in a practical setting due to perceived computational complexity. Here, for neuromuscular (electromyography) and brainwave (electroencephalography) signals acquired from a transhumeral amputee, a computationally efficient time domain signal decomposition method based on a series of heuristics was applied to process the acquired signals before feature extraction. The results showed an improvement in motion intent decoding prowess for the proposed time-domain-based signal decomposition across four different classifiers for both the neuromuscular and brain wave signals when compared to the WD and the raw signal.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

In recent years, the electroencephalography (EEG) brain-computer interface (BCI) has been researched in the area of upper-limb prosthesis control due to the promise of being able to record neurological signals which follow activation patterns in the cortex directly from the brain with non-invasive electrodes. This is seen as a way of bypassing the limitation posed by acquiring neuromuscular signals predominantly with electromyography (EMG) directly from the stump, which possesses residual limb anatomy post-amputation. In this study, the sequential forward selection algorithm to form a 10-optimal-channel representation, alongside an extended signal feature vector was applied, to investigate the motion intent decoding performance of EMG-only, EEG-only, and a fused EMG-EEG sensing configuration for four transhumeral amputees with varying stump lengths. The results showed a considerable improvement for the EMG-only configuration with the advanced feature vector, but only a small increase for the EEG-only, and thus a marginal improvement when information from both signals was fused together. This is likely due to the EEG requiring a greater number of channels spread across the skull to provide a reliable intent decoding. Further work will now involve optimisation studies to find a greater representation of electrode representation and parsimony, to minimise the number of channels while boosting motion intent decoding accuracy. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.