Despite not recording directly from neural cells, the surface electromyogram (EMG) signal contains information on the neural drive to muscles, i.e., the spike trains of motor neurons. Using this property, myoelectric control consists of the recording of EMG signals for extracting control signals to command external devices, such as hand prostheses. In commercial control systems, the intensity of muscle activity is extracted from the EMG and used for single degrees of freedom activation (direct control). Over the past 60 years, academic research has progressed to more sophisticated approaches but, surprisingly, none of these academic achievements has been implemented in commercial systems so far. We provide an overview of both commercial and academic myoelectric control systems and we analyze their performance with respect to the characteristics of the ideal myocontroller. Classic and relatively novel academic methods are described, including techniques for simultaneous and proportional control of multiple degrees of freedom and the use of individual motor neuron spike trains for direct control. The conclusion is that the gap between industry and academia is due to the relatively small functional improvement in daily situations that academic systems offer, despite the promising laboratory results, at the expense of a substantial reduction in robustness. None of the systems so far proposed in the literature fulfills all the important criteria needed for widespread acceptance by the patients, i.e. intuitive, closed-loop, adaptive, and robust real-time ( 200 ms delay) control, minimal number of recording electrodes with low sensitivity to repositioning, minimal training, limited complexity and low consumption. Nonetheless, in recent years, important efforts have been invested in matching these criteria, with relevant steps forwards.

The Extraction of Neural Information from the Surface EMG for the Control of Upper-Limb Prostheses: Emerging Avenues and Challenges

An efficient and flexible dictionary structure is proposed for sparse and redundant signal representation. The proposed sparse dictionary is based on a sparsity model of the dictionary atoms over a base dictionary, and takes the form D = ? A, where ? is a fixed base dictionary and A is sparse. The sparse dictionary provides efficient forward and adjoint operators, has a compact representation, and can be effectively trained from given example data. In this, the sparse structure bridges the gap between implicit dictionaries, which have efficient implementations yet lack adaptability, and explicit dictionaries, which are fully adaptable but non-efficient and costly to deploy. In this paper, we discuss the advantages of sparse dictionaries, and present an efficient algorithm for training them. We demonstrate the advantages of the proposed structure for 3-D image denoising.

/pdf/double-sparsity-learning-sparse-dictionaries-for-sparse-1sikebnnor.pdf

Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation

In pattern recognition-based myoelectric control, high accuracy for multiple discriminated motions is presented in most of related literature. However, there is a gap between the classification accuracy and the usability of practical applications of myoelectric control, especially the effect of long-term usage. This paper proposes and investigates the behavior of fifty time-domain and frequency-domain features to classify ten upper limb motions using electromyographic data recorded during 21days. The most stable single feature and multiple feature sets are presented with the optimum configuration of myoelectric control, i.e. data segmentation and classifier. The result shows that sample entropy (SampEn) outperforms other features when compared using linear discriminant analysis (LDA), a robust classifier. The averaged test classification accuracy is 93.37%, when trained in only initial first day. It brings only 2.45% decrease compared with retraining schemes. Increasing number of features to four, which consists of SampEn, the fourth order cepstrum coefficients, root mean square and waveform length, increase the classification accuracy to 98.87%. The proposed techniques achieve to maintain the high accuracy without the retraining scheme. Additionally, this continuous classification allows the real-time operation.

http://iihm.imag.fr/publs/2014/ESWA-Draft.pdf

EMG feature evaluation for improving myoelectric pattern recognition robustness

In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data. However, within the field of electromyography-based gesture recognition, deep learning algorithms are seldom employed as they require an unreasonable amount of effort from a single person, to generate tens of thousands of examples. This paper’s hypothesis is that general, informative features can be learned from the large amounts of data generated by aggregating the signals of multiple users, thus reducing the recording burden while enhancing gesture recognition. Consequently, this paper proposes applying transfer learning on aggregated data from multiple users while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets. Two datasets comprised 19 and 17 able-bodied participants, respectively (the first one is employed for pre-training), were recorded for this work, using the Myo armband. A third Myo armband dataset was taken from the NinaPro database and is comprised ten able-bodied participants. Three different deep learning networks employing three different modalities as input (raw EMG, spectrograms, and continuous wavelet transform (CWT)) are tested on the second and third dataset. The proposed transfer learning scheme is shown to systematically and significantly enhance the performance for all three networks on the two datasets, achieving an offline accuracy of 98.31% for 7 gestures over 17 participants for the CWT-based ConvNet and 68.98% for 18 gestures over 10 participants for the raw EMG-based ConvNet. Finally, a use-case study employing eight able-bodied participants suggests that real-time feedback allows users to adapt their muscle activation strategy which reduces the degradation in accuracy normally experienced over time.

/pdf/deep-learning-for-electromyographic-hand-gesture-signal-35yh5gi4gl.pdf

Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

Several efforts have been carried out to enhance dexterous hand prosthesis control by impaired individuals. Choosing which voluntary signal to use for control purposes is a critical element to achieve this goal. This review presents and discusses the recent results achieved by using electromyographic signals, recorded either with surface (sEMG) or intramuscular (iEMG) electrodes, and electroneurographic (ENG) signals. The potential benefits and shortcomings of the different approaches are described with a particular attention to the definition of all the steps required to achieve an effective hand prosthesis control in the different cases. Finally, a possible roadmap in the field is also presented.

/pdf/control-of-hand-prostheses-using-peripheral-information-3amjn63ibu.pdf

Control of Hand Prostheses Using Peripheral Information

OBJECTIVE
The development of a novel collision technique for assessment of the activation order of electrically activated nerve fibers, which is an important question in functional electrical therapy or for interpretation of results of motor unit number estimates.


METHODS
Compound muscle action potentials were recorded with the belly-tendon configuration from the abductor digiti minimi. A novel modified Hopf's collision technique was applied on ten healthy male subjects to determine the distributions of conduction velocities (DCV) of all ulnar nerve fibers and of the fibers activated by electrical stimuli eliciting 20%, 50%, and 80% of the maximal muscle response.


RESULTS
The maximum nerve conduction velocity was (means+/-SE) 64.1+/-0.85m/s. The median conduction velocity of estimated DCV was 58.9+/-0.97m/s (stimulus at 20%), 58.0+/-0.98m/s (50%), 57.2+/-0.91m/s (80%), and 56.5+/-0.84m/s (whole nerve) (all different between each other, P<0.001).


CONCLUSIONS
The proposed collision technique allows the assessment of nerve conduction velocity distributions at maximal and sub-maximal stimulation levels and provided evidence for an inverse activation order of nerve fibers with electrical stimulation.


SIGNIFICANCE
The excessive fatigue seen with nerve electrical stimulation can be explained by a preferential activation of large diameter nerve fibers. The motor units first activated with electrical stimulation are likely not representative of the motor unit pool in the muscle, which poses limitations in the reliability of some of the proposed methods for motor unit counting.

The recruitment order of electrically activated motor neuron investigated with a novel collision technique

Multi-Day Real-time Myoelectric Control using Intramuscular EMG

Objective. Despite decades of research on central processing of pain, there are still several unanswered questions, in particular regarding the brain regions that may contribute to this alerting sensation. Since it is generally accepted that more than one cortical area is responsible for pain processing, there is an increasing focus on the interaction between areas known to be involved.Approach. In this study, we aimed to investigate the bidirectional information flow from the primary somatosensory cortex (SI) to the anterior cingulate cortex (ACC) in an animal model of neuropathic pain.19 rats (nine controls and ten intervention) had an intracortical electrode implanted with six pins in SI and six pins in ACC, and a cuff stimulation electrode around the sciatic nerve. The intervention rats were subjected to the spared nerve injury (SNI) after baseline recordings. Electrical stimulation at three intensities of both noxious and non-noxious stimulation was used to record electrically evoked cortical potentials. To investigate information flow, two connectivity measures were used: phase lag index (PLI) and granger prediction (GP). The rats were anesthetized during the entire study.Main results. Immediately after the intervention (<5 min after intervention), the high frequency (γandγ+) PLI was significantly decreased compared to controls. In the last recording cycle (3-4 h after intervention), the GP increased consistently in the intervention group. Peripheral nerve injury, as a model of neuropathic pain, resulted in an immediate decrease in information flow between SI and ACC, possibly due to decreased sensory input from the injured nerve. Hours after injury, the connectivity between SI and ACC increased, likely indicating hypersensitivity of this pathway.Significance. We have shown that both a directed and non-directed connectivity between SI and ACC approach can be used to show the acute changes resulting from the SNI model.

https://iopscience.iop.org/article/10.1088/1741-2552/abfeb9/pdf

Altered evoked low-frequency connectivity from SI to ACC following nerve injury in rats.

surface EMG variables Motor unit recruitment strategies investigated by

Linear discriminant analysis (LDA) is widely used for classification of myoelectric signals and it has been shown to outperform simple classifiers such as k-Nearest Neighbour (kNN). However the normality assumption of the LDA may cause its performance to decrease when the distribution of the feature space is far from Gaussian. In this study we investigate whether nonparametric discriminant (NDA) projections in combination with kNN classifiers can significantly decrease the classification error. Data sets based on both surface and intramuscular electromyography (EMG) were used in order to solve classification problems of up to 9 classes, including simultaneous movements. Results showed that in all data sets, the classification error was significantly lower when using NDA projections compared with LDA.

Ernest Nlandu Kamavuako

Papers

The recruitment order of electrically activated motor neuron investigated with a novel collision technique

Multi-Day Real-time Myoelectric Control using Intramuscular EMG

Altered evoked low-frequency connectivity from SI to ACC following nerve injury in rats.

surface EMG variables Motor unit recruitment strategies investigated by

Nonparametric discriminant projections for improved myoelectric classification