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

The aim of this randomized controlled crossover study was to investigate the effect of a bout of unaccustomed eccentric exercise (ECC) followed by a consecutive bout of the same intensity on local muscle blood flow, amplitude, and frequency of the electromyographic (EMG) signal from the exercised tibialis anterior muscle. Sixteen healthy male participants (age, 25.7 (0.6) years; body mass index 24.8 (1) kg·m(-2) participated in this study. Two identical bouts of high-intensity ECC were performed on the tibialis anterior muscle 7 days apart. Control sessions involving no exercise were performed 4 weeks either before or after the exercise sessions. Changes in local total blood flow [ΔtHb], EMG root mean square, and median power frequency were recorded during isometric maximum voluntary contraction of ankle dorsiflexion. Measurements were performed before, immediately after, and the day after both ECCs (ECC1 and ECC2). The participants rested quietly in a chair in the control session. Eccentric exercise 1 led to a significant decrease in [ΔtHb] on the day after (p ≤ 0.05), whereas ECC2 did not. Median power frequency decreased significantly in ECC2 compared with ECC1 (p < 0.01). Root mean square was unchanged in all the instants. The present study showed that adaptation is depicted in the local muscle blood flow and the frequency contents of the EMG after an unaccustomed ECC inducing muscle soreness. These alterations provide a potential mechanism for a rapid adaptation, which decreases susceptibility of the muscle to develop further soreness in the subsequent ECC bout.

Adaptation of local muscle blood flow and surface electromyography to repeated bouts of eccentric exercise.

People suffering from neuromuscular disorders such as locked-in syndrome (LIS) are left in a paralyzed state with preserved awareness and cognition In this study, it was hypothesized that changes in local hemodynamic activity, due to the activation of Broca's area during overt/covert speech, can be harnessed to create an intuitive Brain Computer Interface based on Near-Infrared Spectroscopy (NIRS) A 12-channel square template was used to cover inferior frontal gyrus and changes in hemoglobin concentration corresponding to six aloud (overtly) and six silently (covertly) spoken words were collected from eight healthy participants An unsupervised feature extraction algorithm was implemented with an optimized support vector machine for classification For all participants, when considering overt and covert classes regardless of words, classification accuracy of 9288 ± 1849% was achieved with oxy-hemoglobin (O2Hb) and 9514 ± 539% with deoxy-hemoglobin (HHb) as a chromophore For a six-active-class problem of overtly spoken words, 8819 ± 712% accuracy was achieved for O2Hb and 7882 ± 1576% for HHb Similarly, for a six-active-class classification of covertly spoken words, 7917 ± 1430% accuracy was achieved with O2Hb and 8681 ± 990% with HHb as an absorber These results indicate that a control paradigm based on covert speech can be reliably implemented into future Brain⁻Computer Interfaces (BCIs) based on NIRS

Classification of Overt and Covert Speech for Near-Infrared Spectroscopy-Based Brain Computer Interface.

Individuals with high-level amputation have a great need for functional prostheses because of their vast functional deficits. Conventional techniques are considered inappropriate for high-level amputees due to the lack of physiologically appropriate muscles. This study investigates how accurate phantom movements (PMs) can be classified from physiologically inappropriate muscles. The study involves a case study of a 42-year-old transhumeral amputee. Suitable PMs and best electrode configuration were identified using the sequential forward selection method and brute-force technique. Using linear discriminant analysis, the best PMs (elbow extension/flexion, wrist supination/pronation) and rest were classified with error ranging from 3% to 0.18% when using 3 to 8 EMG channels respectively. A completion rate of 93 % was obtained during a targeted achievement control test in a virtual reality environment. This case indicates that a proximal transhumeral amputee can generate muscle activation patterns related to distinct PMs; and these PMs can be decoded from physiologically inappropriate muscles.

Phantom movements from physiologically inappropriate muscles: A case study with a high transhumeral amputee

The surface EMG (sEMG) has been used as control source for upper limb prosthetics since decades. Previous studies suggested that intramuscular EMG showed promising results for upper limb prosthetics. This study investigates the strength of combined surface and intramuscular EMG (cEMG) for improved myoelectric control. Five able-bodied subjects and three transradial amputees were evaluated using offline classification error as performance metric. Six surface and intramuscular channels were recorded concurrently from each subject for seven consecutive days and Stacked sparse autoencoders (SSAE) and LDA classifiers were used for classification. As a control source, either sEMG channels were used or combined channels were used with reduced features using PCA. In the within session analysis, cEMG $( 2.21 \pm 1.19${%) outperformed the sEMG ($4.63 \pm 2.07${%) for both able-bodied and amputee subjects using SSAE. For between session analysis, cEMG outperformed the sEMG for both able-bodied and amputee subjects with percentage points difference of 7.93. These results imply cEMG can significantly improve the performance of pattern recognition based myoelectric control scheme for amputee subjects too and further improvement can be made by utilizing SSAE which show improved performance as compared to LDA.

Performance of Combined Surface and Intramuscular EMG for Classification of Hand Movements

Deep learning techniques are the future trend for designing heart sound classification methods, making conventional heart sound segmentation dispensable. However, despite using fixed signal duration for training, no study has assessed its effect on the final performance in detail. Therefore, this study aims at analysing the duration effect on the commonly used deep learning methods to provide insight for future studies in data processing, classifier, and feature selection. The results of this study revealed that (1) very short heart sound signal duration (1 s) weakens the performance of Recurrent Neural Networks (RNNs), whereas no apparent decrease in the tested Convolutional Neural Network (CNN) model was found. (2) RNN outperformed CNN using Mel-frequency cepstrum coefficients (MFCCs) as features. There was no difference between RNN models (LSTM, BiLSTM, GRU, or BiGRU). (3) Adding dynamic information (∆ and ∆²MFCCs) of the heart sound as a feature did not improve the RNNs’ performance, and the improvement on CNN was also minimal (≤2.5% in MAcc). The findings provided a theoretical basis for further heart sound classification using deep learning techniques when selecting the input length.

/pdf/the-effect-of-signal-duration-on-the-classification-of-heart-eq1jtgho.pdf

Ernest Nlandu Kamavuako

Papers

Adaptation of local muscle blood flow and surface electromyography to repeated bouts of eccentric exercise.

Classification of Overt and Covert Speech for Near-Infrared Spectroscopy-Based Brain Computer Interface.

Phantom movements from physiologically inappropriate muscles: A case study with a high transhumeral amputee

Performance of Combined Surface and Intramuscular EMG for Classification of Hand Movements

The Effect of Signal Duration on the Classification of Heart Sounds: A Deep Learning Approach