Ales Holobar

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

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.

Accurate identification of motor unit discharge patterns from high-density surface EMG and validation with a novel signal-based performance metric

OBJECTIVE A signal-based metric for assessment of accuracy of motor unit (MU) identification from high-density surface electromyograms (EMG) is introduced. This metric, so-called pulse-to-noise-ratio (PNR), is computationally efficient, does not require any additional experimental costs and can be applied to every MU that is identified by the previously developed convolution kernel compensation technique. APPROACH The analytical derivation of the newly introduced metric is provided, along with its extensive experimental validation on both synthetic and experimental surface EMG signals with signal-to-noise ratios ranging from 0 to 20 dB and muscle contraction forces from 5% to 70% of the maximum voluntary contraction. MAIN RESULTS In all the experimental and simulated signals, the newly introduced metric correlated significantly with both sensitivity and false alarm rate in identification of MU discharges. Practically all the MUs with PNR > 30 dB exhibited sensitivity >90% and false alarm rates <2%. Therefore, a threshold of 30 dB in PNR can be used as a simple method for selecting only reliably decomposed units. SIGNIFICANCE The newly introduced metric is considered a robust and reliable indicator of accuracy of MU identification. The study also shows that high-density surface EMG can be reliably decomposed at contraction forces as high as 70% of the maximum.

Adjustments Differ Among Low-Threshold Motor Units During Intermittent, Isometric Contractions

We investigated the changes in muscle fiber conduction velocity, recruitment and derecruitment thresholds, and discharge rate of low-threshold motor units during a series of ramp contractions. The aim was to compare the adjustments in motor unit activity relative to the duration that each motor unit was active during the task. Multichannel surface electromyographic (EMG) signals were recorded from the abductor pollicis brevis muscle of eight healthy men during 12-s contractions (n = 25) in which the force increased and decreased linearly from 0 to 10% of the maximum. The maximal force exhibited a modest decline (8.5 +/- 9.3%; P < 0.05) at the end of the task. The discharge times of 73 motor units that were active for 16-98% of the time during the first five contractions were identified throughout the task by decomposition of the EMG signals. Action potential conduction velocity decreased during the task by a greater amount for motor units that were initially active for >70% of the time compared with that of less active motor units. Moreover, recruitment and derecruitment thresholds increased for these most active motor units, whereas the thresholds decreased for the less active motor units. Another 18 motor units were recruited at an average of 171 +/- 32 s after the beginning of the task. The recruitment and derecruitment thresholds of these units decreased during the task, but muscle fiber conduction velocity did not change. These results indicate that low-threshold motor units exhibit individual adjustments in muscle fiber conduction velocity and motor neuron activation that depended on the relative duration of activity during intermittent contractions.

Non-invasive characterization of motor unit behaviour in pathological tremor

This paper presents the fully automatic identification of motor unit spike trains from high-density surface electromyograms (EMG) in pathological tremor. First, a mathematical derivation is provided to theoretically prove the possibility of decomposing noise-free high-density surface EMG signals into motor unit spike trains with high correlation, which are typical of tremor contractions. Further, the proposed decomposition method is tested on simulated signals with different levels of noise and on experimental signals from 14 tremor-affected patients. In the case of simulated tremor with central frequency ranging from 5 Hz to 11 Hz and signal-to-noise ratio of 20 dB, the method identified ∼8 motor units per contraction with sensitivity in spike timing identification ≥ 95% and false alarm and miss rates ≤ 5%. In experimental signals, the number of identified motor units varied substantially (range 0-21) across patients and contraction types, as expected. The behaviour of the identified motor units was consistent with previous data obtained by intramuscular EMG decomposition. These results demonstrate for the first time the possibility of a fully non-invasive investigation of motor unit behaviour in tremor-affected patients. The method provides a new means for physiological investigations of pathological tremor.

Examination of Poststroke Alteration in Motor Unit Firing Behavior Using High-Density Surface EMG Decomposition

Recent advances in high-density surface electromyogram (EMG) decomposition have made it a feasible task to discriminate single motor unit activity from surface EMG interference patterns, thus providing a noninvasive approach for examination of motor unit control properties. In the current study, we applied high-density surface EMG recording and decomposition techniques to assess motor unit firing behavior alterations poststroke. Surface EMG signals were collected using a 64-channel 2-D electrode array from the paretic and contralateral first dorsal interosseous (FDI) muscles of nine hemiparetic stroke subjects at different isometric discrete contraction levels between 2 to 10 N with a 2 N increment step. Motor unit firing rates were extracted through decomposition of the high-density surface EMG signals and compared between paretic and contralateral muscles. Across the nine tested subjects, paretic FDI muscles showed decreased motor unit firing rates compared with contralateral muscles at different contraction levels. Regression analysis indicated a linear relation between the mean motor unit firing rate and the muscle contraction level for both paretic and contralateral muscles (p <; 0.001), with the former demonstrating a lower increment rate (0.32 pulses per second (pps)/N) compared with the latter (0.67 pps/N). The coefficient of variation (averaged over the contraction levels) of the motor unit firing rates for the paretic muscles (0.21 ± 0.012) was significantly higher than for the contralateral muscles (0.17 ± 0.014) (p <; 0.05). This study provides direct evidence of motor unit firing behavior alterations poststroke using surface EMG, which can be an important factor contributing to hemiparetic muscle weakness.

Accuracy assessment of CKC high-density surface EMG decomposition in biceps femoris muscle

The aim of this study was to assess the accuracy of the convolution kernel compensation (CKC) method in decomposing high-density surface EMG (HDsEMG) signals from the pennate biceps femoris long-head muscle. Although the CKC method has already been thoroughly assessed in parallel-fibered muscles, there are several factors that could hinder its performance in pennate muscles. Namely, HDsEMG signals from pennate and parallel-fibered muscles differ considerably in terms of the number of detectable motor units (MUs) and the spatial distribution of the motor-unit action potentials (MUAPs). In this study, monopolar surface EMG signals were recorded from five normal subjects during low-force voluntary isometric contractions using a 92-channel electrode grid with 8 mm inter-electrode distances. Intramuscular EMG (iEMG) signals were recorded concurrently using monopolar needles. The HDsEMG and iEMG signals were independently decomposed into MUAP trains, and the iEMG results were verified using a rigorous a posteriori statistical analysis. HDsEMG decomposition identified from 2 to 30 MUAP trains per contraction. 3 ± 2 of these trains were also reliably detected by iEMG decomposition. The measured CKC decomposition accuracy of these common trains over a selected 10 s interval was 91.5 ± 5.8%. The other trains were not assessed. The significant factors that affected CKC decomposition accuracy were the number of HDsEMG channels that were free of technical artifact and the distinguishability of the MUAPs in the HDsEMG signal (P < 0.05). These results show that the CKC method reliably identifies at least a subset of MUAP trains in HDsEMG signals from low force contractions in pennate muscles.

An approach to surface EMG decomposition based on higher-order cumulants

We are addressing a possible approach to the decomposition of surface electromyograms (SEMGs). It is based on higher-order cumulants implemented in a two-step procedure. Firstly, a multivariate version of the w-slice method is applied in order to extract coarse approximations of motor-unit action potentials (MUAPs) out of the measured SEMGs. Secondly, these coarse estimates are refined by modified Newton-Gauss iteration to achieve an optimum fit of the model-based and the observation-based cumulant estimates. All the necessary conditions are derived theoretically and, afterwards, implemented in simulation runs in order to prove the decomposition power of the proposed approach on synthetic SEMGs. The first-norm difference between the original and the decomposed MUAPs, obtained at the signal length of 102400 samples and expressed in percentage of the MUAP amplitude span, yields 5.4% in the noise-free case, 6.0% with a signal-to-noise ratio (SNR) of 10dB, and 6.5% with a SNR of 0 dB.

Motor unit firing pattern of vastus lateralis muscle in type 2 diabetes mellitus patients.

Introduction: We investigated the motor unit (MU) firing pattern in type 2 diabetes mellitus (T2DM) patients by means of multichannel surface electromyography (SEMG). Methods: Eight T2DM patients and 8 age‐matched, healthy men performed a ramp‐up contraction to 20% of maximal voluntary contraction (MVC). They also performed a sustained contraction at 10% of MVC during isometric knee extension. Multichannel SEMG signals recorded from the vastus lateralis muscle were decomposed with the convolution kernel compensation technique to extract individual MU firing patterns. Results: During the ramp contraction, the extent of MU firing modulation was significantly attenuated in T2DM. Variability of MU firing rate was significantly higher in T2DM at later periods during the sustained contraction. Conclusions: Our findings suggest that T2DM patients manifest characteristic MU activity patterns due possibly to some degree of neuromuscular impairment affecting the integrity of MU firing modulation. Muscle Nerve 48:806–813, 2013

Origin and development of muscle cramps

Cramps are sudden, involuntary, painful muscle contractions. Their pathophysiology remains poorly understood. One hypothesis is that cramps result from changes in motor neuron excitability (central origin). Another hypothesis is that they result from spontaneous discharges of the motor nerves (peripheral origin). The central origin hypothesis has been supported by recent experimental findings, whose implications for understanding cramp contractions are discussed.

Characterization of Human Motor Units From Surface EMG Decomposition

Motor units are the smallest functional units of our movements. The study of their activation provides a window into the mechanisms of neural control of movement in humans. The classic methods for motor unit investigations date to several decades ago. They are based on invasive recordings with selective needle or wire electrodes. Conversely, the noninvasive (surface) EMG has been commonly processed as an interference signal, with the extraction of its global characteristics, e.g., amplitude. These characteristics, however, are only crudely associated to the underlying motor unit activities. In the last decade, methods have been proposed for reliably extracting individual motor unit activities from the interference surface EMG signal. We describe these methods in this review, with a focus on blind source separation (BSS) and techniques used on decomposed EMG signals. For example, from the motor unit discharge timings, information can be extracted regarding the synaptic input received by the corresponding motor neurons. In reviewing these methods, we also provide examples of applications in representative conditions, such as pathological tremor. In conclusion, we provide an overview of processing methods of the surface EMG signal that allow a reliable characterization of individual motor units in vivo in humans.