609 results found
Dideriksen JL, Del Vecchio A, Farina D, 2020, Neural and muscular determinants of maximal rate of force development., J Neurophysiol, Vol: 123, Pages: 149-157
The ability to produce rapid forces requires quick motor unit recruitment, high motor unit discharge rates, and fast motor unit force twitches. The relative importance of these parameters for maximum rate of force development (RFD), however, is poorly understood. In this study, we systematically investigated these relationships using a computational model of motor unit pool activity and force. Across simulations, neural and muscular properties were systematically varied in experimentally observed ranges. Motor units were recruited over an interval starting from contraction onset (range: 22-233 ms). Upon recruitment, discharge rates declined from an initial rate (range: 89-212 pulses per second), with varying likelihood of doublet (interspike interval of 3 ms; range: 0-50%). Finally, muscular adaptations were modeled by changing average twitch contraction time (range: 42-78 ms). Spectral analysis showed that the effective neural drive to the simulated muscle had smaller bandwidths than the average motor unit twitch, indicating that the bandwidth of the motor output, and thus the capacity for explosive force, was limited mainly by neural properties. The simulated RFD increased by 1,050 ± 281% maximal voluntary contraction force per second from the longest to the shortest recruitment interval. This effect was more than fourfold higher than the effect of increasing the initial discharge rate, more than fivefold higher than the effect of increasing the chance of doublets, and more than sixfold higher than the effect of decreasing twitch contraction times. The simulated results suggest that the physiological variation of the rate by which motor units are recruited during ballistic contractions is the main determinant for the variability in RFD across individuals.NEW & NOTEWORTHY An important limitation of human performance is the ability to generate explosive movements by means of rapid development of muscle force. The physiological determinants o
Chen C, Yu Y, Ma S, et al., 2020, Hand gesture recognition based on motor unit spike trains decoded from high-density electromyography, BIOMEDICAL SIGNAL PROCESSING AND CONTROL, Vol: 55, ISSN: 1746-8094
Wilke MA, Niethammer C, Meyer B, et al., 2019, Psychometric characterization of incidental feedback sources during grasping with a hand prosthesis., J Neuroeng Rehabil, Vol: 16
BACKGROUND: A prosthetic system should ideally reinstate the bidirectional communication between the user's brain and its end effector by restoring both motor and sensory functions lost after an amputation. However, current commercial prostheses generally do not incorporate somatosensory feedback. Even without explicit feedback, grasping using a prosthesis partly relies on sensory information. Indeed, the prosthesis operation is characterized by visual and sound cues that could be exploited by the user to estimate the prosthesis state. However, the quality of this incidental feedback has not been objectively evaluated. METHODS: In this study, the psychometric properties of the auditory and visual feedback of prosthesis motion were assessed and compared to that of a vibro-tactile interface. Twelve able-bodied subjects passively observed prosthesis closing and grasping an object, and they were asked to discriminate (experiment I) or estimate (experiment II) the closing velocity of the prosthesis using visual (VIS), acoustic (SND), or combined (VIS + SND) feedback. In experiment II, the subjects performed the task also with a vibrotactile stimulus (VIB) delivered using a single tactor. The outcome measures for the discrimination and estimation experiments were just noticeable difference (JND) and median absolute estimation error (MAE), respectively. RESULTS: The results demonstrated that the incidental sources provided a remarkably good discrimination and estimation of the closing velocity, significantly outperforming the vibrotactile feedback. Using incidental sources, the subjects could discriminate almost the minimum possible increment/decrement in velocity that could be commanded to the prosthesis (median JND < 2% for SND and VIS + SND). Similarly, the median MAE in estimating the prosthesis velocity randomly commanded from the full working range was also low, i.e., approximately 5% in SND and VIS + SND. CO
Vecchio AD, Farina D, 2019, Interfacing the neural output of the spinal cord: robust and reliable longitudinal identification of motor neurons in humans., J Neural Eng, Vol: 17
OBJECTIVE: Non-invasive electromyographic techniques can detect action potentials from muscle units with high spatial dimensionality. These technologies allow the decoding of large samples of motor units by using high-density grids of electrodes that are placed on the skin overlying contracting muscles and therefore provide a non-invasive representation of the human spinal cord output. APPROACH: From a sample of >1200 decoded motor neurons, we show that motor neuron activity can be identified in humans in the full muscle recruitment range with high accuracy. MAIN RESULTS: After showing the validity of decomposition with novel test parameters, we demonstrate that the same motor neurons can be tracked over a period of one-month, which allows for the longitudinal analysis of individual human neural cells. SIGNIFICANCE: These results show the potential of an accurate and reliable assessment of large populations of motor neurons in physiological investigations. We discuss the potential of this non-invasive neural interfacing technology for the study of the neural determinants of movement and man-machine interfacing.
Germer CM, Del Vecchio A, Negro F, et al., 2019, Neurophysiological correlates of force control improvement induced by sinusoidal vibrotactile stimulation., J Neural Eng
OBJECTIVE: An optimal level of vibrotactile stimulation has been shown to improve sensorimotor control in healthy and diseased individuals. However, the underlying neurophysiological mechanisms behind the enhanced motor performance caused by vibrotactile stimulation are yet to be fully understood. Therefore, here we aim to evaluate the effect of a cutaneous vibration on the firing behavior of motor units in a condition of improved force steadiness. APPROACH: Participants performed a visuomotor task, which consisted of low-intensity isometric contractions of the first dorsal interosseous (FDI) muscle, while sinusoidal (175Hz) vibrotactile stimuli with different intensities were applied to the index finger. High-density surface electromyogram was recorded from the FDI muscle, and a decomposition algorithm was used to extract the motor unit spike trains. Additionally, computer simulations were performed using a multiscale neuromuscular model to provide a potential explanation for the experimental findings. MAIN RESULTS: Experimental outcomes showed that an optimal level of vibration significantly improved force steadiness (estimated as the coefficient of variation of force). The decreased force variability was accompanied by a reduction in the variability of the smoothed cumulative spike train (as an estimation of the neural drive to the muscle), and the proportion of common inputs to the FDI motor nucleus. However, the interspike interval variability did not change significantly with the vibration. A mathematical approach, together with computer simulation results suggested that vibrotactile stimulation would reduce the variance of the common synaptic input to the motor neuron pool, thereby decreasing the low frequency fluctuations of the neural drive to the muscle and force steadiness. SIGNIFICANCE: Our results demonstrate that the decreased variability in common input accounts for the enhancement in force control induced by vibrotactile stimulation.
Del Vecchio A, Germer CM, Elias LA, et al., 2019, The human central nervous system transmits common synaptic inputs to distinct motor neuron pools during non-synergistic digit actions., J Physiol, Vol: 597, Pages: 5935-5948
KEY POINTS: Neural connectivity between distinct motor neuronal modules in the spinal cord is classically studied through electrical stimulation or multi-muscle EMG recordings. We quantified the strength of correlation in the activity of two distinct populations of motor neurons innervating the thenar and first dorsal interosseous muscles during tasks that required the two hand muscles to exert matched or un-matched forces in different directions. We show that when the two hand muscles are concurrently activated, synaptic input to the two motor neuron pools is shared across all frequency bandwidths (representing cortical and spinal input) associated with force control. The observed connectivity indicates that motor neuron pools receive common input even when digit actions do not belong to a common behavioural repertoire. ABSTRACT: Neural connectivity between distinct motor neuronal modules in the spinal cord is classically studied through electrical stimulation or multi-muscle EMG recordings. Here we quantify the strength of correlation in the activity of two distinct populations of motor neurons innervating the thenar and first dorsal interosseous muscles in humans during voluntary contractions. To remove confounds associated with previous studies, we used a task that required the two hand muscles to exert matched or un-matched forces in different directions. Despite the force production task consisting of uncommon digit force coordination patterns, we found that synaptic input to motor neurons is shared across all frequency bands, reflecting cortical and spinal inputs associated with force control. The coherence between discharge timings of the two pools of motor neurons was significant at the delta (0-5 Hz), alpha (5-15 Hz) and beta (15-35 Hz) bands (P < 0.05). These results suggest that correlated input to motor neurons of two hand muscles can occur even during tasks not belonging to a common behavioural repertoire and despite lack of
Casolo A, Farina D, Falla D, et al., 2019, Strength training Increases conduction velocity of high-threshold motor units., Medicine and Science in Sports and Exercise, ISSN: 0195-9131
PURPOSE: Motor unit conduction velocity (MUCV) represents the propagation velocity of action potentials along the muscle fibres innervated by individual motor neurons and indirectly reflects the electrophysiological properties of the sarcolemma. In this study, we investigated the effect of a 4-week strength training intervention on the peripheral properties (MUCV and motor unit action potential amplitude, RMSMU) of populations of longitudinally tracked motor units (MUs). METHODS: The adjustments exhibited by 12 individuals who participated in the training (INT) were compared with 12 controls (CON). Strength training involved ballistic (4x10) and sustained (3x10) isometric ankle dorsi flexions. Measurement sessions involved the recordings of maximal voluntary isometric force (MViF) and submaximal isometric ramp contractions, while high-density surface EMG (HDsEMG) was recorded from the tibialis anterior. HDsEMG signals were decomposed into individual MU discharge timings and MUs were tracked across the intervention. RESULTS: MViF (+14.1%, P=0.003) and average MUCV (+3.00%, P=0.028) increased in the INT group, while normalized MUs recruitment threshold (RT) decreased (-14.9%, P=0.001). The slope (rate of change) of the regression between MUCV and MUs RT increased only in the INT group (+32.6%, P=0.028), indicating a progressive greater increase in MUCV for higher-threshold MUs. The intercept (initial value) of MUCV did not change following the intervention (P=0.568). The association between RMSMU and MUs RT was not altered by the training. CONCLUSION: The increase in the rate of change in MUCV as a function of MU recruitment threshold, but not the initial value of MUCV, suggests that short-term strength training elicits specific adaptations in the electrophysiological properties of the muscle fibre membrane in high-threshold motor units.
Puttaraksa G, Muceli S, Alvaro Gallego J, et al., 2019, Voluntary and tremorogenic inputs to motor neuron pools of agonist/antagonist muscles in essential tremor patients, JOURNAL OF NEUROPHYSIOLOGY, Vol: 122, Pages: 2043-2053, ISSN: 0022-3077
Aliakbaryhosseinabadi S, Kamavuako EN, Jiang N, et al., 2019, Classification of Movement Preparation Between Attended and Distracted Self-Paced Motor Tasks., IEEE Trans Biomed Eng, Vol: 66, Pages: 3060-3071
OBJECTIVE: Brain-computer interface (BCI) systems aim to control external devices by using brain signals. The performance of these systems is influenced by the user's mental state, such as attention. In this study, we classified two attention states to a target task (attended and distracted task level) while attention to the task is altered by one of three types of distractors. METHODS: A total of 27 participants were allocated into three experimental groups and exposed to one type of distractor. An attended condition that was the same across the three groups comprised only the main task execution (self-paced dorsiflexion) while the distracted condition was concurrent execution of the main task and an oddball task (dual-task condition). Electroencephalography signals were recorded from 28 electrodes to classify the two attention states of attended or distracted task conditions by extracting temporal and spectral features. RESULTS: The results showed that the ensemble classification accuracy using the combination of temporal and spectral features (spectro-temporal features, 82.3 ± 2.7%) was greater than using temporal (69 ± 2.2%) and spectral (80.3 ± 2.6%) features separately. The classification accuracy was computed using a combination of different channel locations, and it was demonstrated that a combination of parietal and centrally located channels was superior for classification of two attention states during movement preparation (parietal channels: 84.6 ± 1.3%, central and parietal channels: 87.2 ± 1.5%). CONCLUSION: It is possible to monitor the users' attention to the task for different types of distractors. SIGNIFICANCE: It has implications for online BCI systems where the requirement is for high accuracy of intention detection.
Thompson CK, Johnson MD, Negro F, et al., 2019, Exogenous neuromodulation of spinal neurons induces beta-band coherence during self-sustained discharge of hind limb motor unit populations., J Appl Physiol (1985), Vol: 127, Pages: 1034-1041
The spontaneous or self-sustained discharge of spinal motoneurons can be observed in both animals and humans. Although the origins of this self-sustained discharge are not fully known, it can be generated by activation of persistent inward currents intrinsic to the motoneuron. If self-sustained discharge is generated exclusively through this intrinsic mechanism, the discharge of individual motor units will be relatively independent of one another. Alternatively, if increased activation of premotor circuits underlies this prolonged discharge of spinal motoneurons, we would expect correlated activity among motoneurons. Our aim is to assess potential synaptic drive by quantifying coherence during self-sustained discharge of spinal motoneurons. Electromyographic activity was collected from 20 decerebrate animals using a 64-channel electrode grid placed on the isolated soleus muscle before and following intrathecal administration of methoxamine, a selective α1-noradrenergic agonist. Sustained muscle activity was recorded and decomposed into the discharge times of ~10-30 concurrently active individual motor units. Consistent with previous reports, the self-sustained discharge of motor units occurred at low mean discharge rates with low-interspike variability. Before methoxamine administration, significant low-frequency coherence (<2 Hz) was observed, while minimal coherence was observed within higher frequency bands. Following intrathecal administration of methoxamine, increases in motor unit discharge rates and strong coherence in both the low-frequency and 15- to 30-Hz beta bands were observed. These data demonstrate beta-band coherence among motor units can be observed through noncortical mechanisms and that neuromodulation of spinal/brainstem neurons greatly influences coherent discharge within spinal motor pools.NEW & NOTEWORTHY The correlated discharge of spinal motoneurons is often used to describe the input to the motor pool. We demonstrate spinal/bra
Besomi M, Hodges PW, Van Dieën J, et al., 2019, Consensus for experimental design in electromyography (CEDE) project: Electrode selection matrix., J Electromyogr Kinesiol, Vol: 48, Pages: 128-144
The Consensus for Experimental Design in Electromyography (CEDE) project is an international initiative which aims to guide decision-making in recording, analysis, and interpretation of electromyographic (EMG) data. The quality of the EMG recording, and validity of its interpretation depend on many characteristics of the recording set-up and analysis procedures. Different electrode types (i.e., surface and intramuscular) will influence the recorded signal and its interpretation. This report presents a matrix to consider the best electrode type selection for recording EMG, and the process undertaken to achieve consensus. Four electrode types were considered: (1) conventional surface electrode, (2) surface matrix or array electrode, (3) fine-wire electrode, and (4) needle electrode. General features, pros, and cons of each electrode type are presented first. This information is followed by recommendations for specific types of muscles, the information that can be estimated, the typical representativeness of the recording and the types of contractions for which the electrode is best suited. This matrix is intended to help researchers when selecting and reporting the electrode type in EMG studies.
Xu R, Dosen S, Jiang N, et al., 2019, Continuous 2D control via state-machine triggered by endogenous sensory discrimination and a fast brain switch, JOURNAL OF NEURAL ENGINEERING, Vol: 16, ISSN: 1741-2560
Rashid U, Niazi IK, Signal N, et al., 2019, Optimal automatic detection of muscle activation intervals., J Electromyogr Kinesiol, Vol: 48, Pages: 103-111
A significant challenge in surface electromyography (sEMG) is the accurate identification of onsets and offsets of muscle activations. Manual labelling and automatic detection are currently used with varying degrees of reliability, accuracy and time efficiency. Automatic methods still require significant manual input to set the optimal parameters for the detection algorithm. These parameters usually need to be adjusted for each individual, muscle and movement task. We propose a method to automatically identify optimal detection parameters in a minimally supervised way. The proposed method solves an optimisation problem that only requires as input the number of activation bursts in the sEMG in a given time interval. This approach was tested on an extended version of the widely adopted double thresholding algorithm, although the optimisation could be applied to any detection algorithm. sEMG data from 22 healthy participants performing a single (ankle dorsiflexion) and a multi-joint (step on/off) task were used for evaluation. Detection rate, concordance, F1 score as an average of sensitivity and precision, degree of over detection, and degree of under detection were used as performance metrices. The proposed method improved the performance of the double thresholding algorithm in multi-joint movement and had the same performance in single joint movement with respect to the performance of the double thresholding algorithm with task specific global parameters. Moreover, the proposed method was robust when an error of up to ±10% was introduced in the number of activation bursts in the optimisation phase regardless of the movement. In conclusion, our optimised method has improved the automation of a sEMG detection algorithm which may reduce the time burden associated with current sEMG processing.
Felici F, Bazzucchi I, Casolo A, et al., 2019, The relative strength of common synaptic input to motor neurons is not a determinant of the maximal rate of force development in humans, Publisher: WILEY, Pages: 25-26, ISSN: 1748-1708
Sturma A, Hruby LA, Farina D, et al., 2019, Structured motor rehabilitation after selective nerve transfers, Jove-Journal of Visualized Experiments, Vol: 150, Pages: 1-11, ISSN: 1940-087X
Here, we present a protocol for the motor rehabilitation of patients with severe nerve injuries and selective nerve transfer surgery. It aims at restoring the motor function proposing several stages in patient education, early-stage therapy after surgery and interventions for rehabilitation after successful re-innervation of the nerve’s target.
Salminger S, Sturma A, Hofer C, et al., 2019, Long-term implant of intramuscular sensors and nerve transfers for wireless control of robotic arms in above-elbow amputees, SCIENCE ROBOTICS, Vol: 4, ISSN: 2470-9476
Durandau G, Farina D, Asín-Prieto G, et al., 2019, Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling., J Neuroeng Rehabil, Vol: 16
BACKGROUND: Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery. METHODS: We have developed a patient-specific computational model of the human musculoskeletal system controlled via neural surrogates, i.e., electromyography-derived neural activations to muscles. The electromyography-driven musculoskeletal model was synthesized into a human-machine interface (HMI) that enabled poststroke and incomplete spinal cord injury patients to voluntarily control multiple joints in a multifunctional robotic exoskeleton in real time. RESULTS: We demonstrated patients' control accuracy across a wide range of lower-extremity motor tasks. Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients. CONCLUSIONS: Continuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. Our proposed methodology may open new avenues for harnessin
Del Vecchio A, Falla D, Felici F, et al., 2019, The relative strength of common synaptic input to motor neurons is not a determinant of the maximal rate of force development in humans, JOURNAL OF APPLIED PHYSIOLOGY, Vol: 127, Pages: 205-214, ISSN: 8750-7587
Stachaczyk M, Atashzar SF, Farina D, 2019, An Online Spectral Information-Enhanced Approach for Artifact Detection and Fault Attentuation in Myoelectric Control., IEEE Int Conf Rehabil Robot, Vol: 2019, Pages: 671-675
In myocontrol of neuroprosthetic devices, multichannel electromyography (EMG) can be used to decode the intended motor command, based on distributed activation patterns of stump muscles. In this regard, the high density EMG (HD-EMG) approach allows for enhancement of the spatiotemporal resolution for motor intention detection. Despite the advantages of relying on several EMG channels, the challenge of high-density electrode systems is the dynamically changing electrode-skin contact impedance, which can affect a considerable number of electrodes over the time of data acquisition. This can result in obtaining unreliable, low-quality EMG recording with a distributed artifact pattern over the grid of EMG sensors. To address this issue, we propose a novel online approach for adaptive information extraction and enhancement for automatic artifact detection and attenuation in HD-EMG-based myocontrol of prosthetic devices. The method is based on an adaptive weighting scheme that modifies the contribution of each HD-EMG channel considering the spectral information content relative to artifacts. The technique (named IE-HD-EMG) was tested as an online pre-conditioning step for a challenging multiclass classification problem of 4-finger activation, using linear discriminant analysis. It is shown that for this application, the proposed IE-HD-EMG technique led to a superior performance in finger activation recognition (79.25% accuracy, 89% sensitivity, 89.15% specificity) in comparison to the conventional HD-EMG recording under the same condition without the proposed approach (56.25% accuracy, 61.3% sensitivity, 67% specificity). Therefore, the proposed technique can have a significant potential to expand the clinical viability of HD-EMG systems.
Yeung D, Farina D, Vujaklija I, 2019, Can Multi-DoF Training Improve Robustness of Muscle Synergy Inspired Myocontrollers?, Pages: 665-670
Non-negative Matrix Factorization (NMF) has been effective in extracting commands from surface electromyography (EMG) for the control of upper-limb prostheses. This approach enables Simultaneous and Proportional Control (SPC) over multiple degrees-of-freedom (DoFs) in a minimally supervised way. Here, like with other myoelectric approaches, robustness remains essential for clinical adoption, with device donning/doffing being a known cause for performance degradation. Previous research has demonstrated that NMF-based myocontrollers, trained on just single-DoF activations, permit a certain degree of user adaptation to a range of disturbances. In this study, we compare this traditional NMF controller with its sparsity constrained variation that allows initialization using both single and combined-DoF activations (NMF-C). The evaluation was done on 12 able bodied participants through a set of online target-reaching tests. Subjects were fitted with an 8-channel bipolar EMG setup, which was shifted by 1cm in both transversal directions throughout the experiments without system retraining. In the baseline condition NMF performed somewhat better than NMFC, but it did suffer more following the electrode repositioning, making the two perform on par. With no significant difference present across the conditions, results suggest that there is no immediate advantage from the naïve inclusion of more comprehensive training sets to the classic synergy-inspired implementation of SPC.
Yeung D, Farina D, Vujaklija I, 2019, Directional Forgetting for Stable Co-Adaptation in Myoelectric Control., Sensors (Basel), Vol: 19
Conventional myoelectric controllers provide a mapping between electromyographic signals and prosthetic functions. However, due to a number of instabilities continuously challenging this process, an initial mapping may require an extended calibration phase with long periods of user-training in order to ensure satisfactory performance. Recently, studies on co-adaptation have highlighted the benefits of concurrent user learning and machine adaptation where systems can cope with deficiencies in the initial model by learning from newly acquired data. However, the success remains highly dependent on careful weighting of these new data. In this study, we proposed a function driven directional forgetting approach to the recursive least-squares algorithm as opposed to the classic exponential forgetting scheme. By only discounting past information in the same direction of the new data, local corrections to the mapping would induce less distortion to other regions. To validate the approach, subjects performed a set of real-time myoelectric tasks over a range of forgetting factors. Results show that directional forgetting with a forgetting factor of 0.995 outperformed exponential forgetting as well as unassisted user learning. Moreover, myoelectric control remained stable after adaptation with directional forgetting over a range of forgetting factors. These results indicate that a directional approach to discounting past training data can improve performance and alleviate sensitivities to parameter selection in recursive adaptation algorithms.
Yu T, Akhmadeev K, Carpentier EL, et al., 2019, Recursive decomposition of electromyographic signals with a varying number of active sources: Bayesian modelling and filtering., IEEE Trans Biomed Eng
OBJECTIVE: This paper describes a sequential decomposition algorithm for single channel intramuscular electromyography (iEMG) generated by a varying number of active motor neurons. METHODS: As in previous work, we establish a Hidden Markov Model of iEMG, in which each motor neuron spike train is modeled as a renewal process with inter-spike intervals following a discrete Weibull law and motor unit action potentials are modeled as impulse responses of linear time-invariant systems with known prior. We then expand this model by introducing an activation vector associated to the state vector of the Hidden Markov Model. This activation vector represents recruitment/derecruitment of motor units and is estimated together with the state vector using Bayesian filtering. Non-stationarity of the model parameters is addressed by means of a sliding window approach, thus making the algorithm adaptive to variations in contraction force and motor unit action potential waveforms. RESULTS: The algorithm was validated using simulated and experimental iEMG signals with varying number of active motor units. The experimental signals were acquired from the tibialis anterior and abductor digiti minimi muscles by fine wire and needle electrodes. The decomposition accuracy in both simulated and experimental signals exceeded 90%. CONCLUSION: The recruitment/derecruitment was successfully tracked by the algorithm. Because of its parallel structure, this algorithm can be efficiently accelerated, which lays the basis for its realtime applications in human-machine interfaces. SIGNIFICANCE: The proposed method substantially broadens the domains of applicabitility of the algorithm.
Dideriksen JL, Farina D, 2019, Amplitude cancellation influences the association between frequency components in the neural drive to muscle and the rectified EMG signal, PLoS Computational Biology, Vol: 15, ISSN: 1553-734X
The rectified surface EMG signal is commonly used as an estimator of the neural drive to muscles and therefore to infer sources of synaptic input to motor neurons. Loss of EMG amplitude due to the overlap of motor unit action potentials (amplitude cancellation), however, may distort the spectrum of the rectified EMG and thereby its correlation with the neural drive. In this study, we investigated the impact of amplitude cancelation on this correlation using analytical derivations and a computational model of motor neuron activity, force, and the EMG signal. First, we demonstrated analytically that an ideal rectified EMG signal without amplitude cancellation (EMGnc) is superior to the actual rectified EMG signal as estimator of the neural drive to muscle. This observation was confirmed by the simulations, as the average coefficient of determination (r2) between the neural drive in the 1–30 Hz band and EMGnc (0.59±0.08) was matched by the correlation between the rectified EMG and the neural drive only when the level of amplitude cancellation was low (<40%) at low contraction levels (<5% of maximum voluntary contraction force; MVC). This correlation, however, decreased linearly with amplitude cancellation (r = -0.83) to values of r2 <0.2 at amplitude cancellation levels >60% (contraction levels >15% MVC). Moreover, the simulations showed that a stronger (i.e. more variable) neural drive implied a stronger correlation between the rectified EMG and the neural drive and that amplitude cancellation distorted this correlation mainly for low-frequency components (<5 Hz) of the neural drive. In conclusion, the results indicate that amplitude cancellation distorts the spectrum of the rectified EMG signal. This implies that valid use of the rectified EMG as an estimator of the neural drive requires low contraction levels and/or strong common synaptic input to the motor neurons.
Del Vecchio A, Negro F, Holobar A, et al., 2019, You are as fast as your motor neurons: speed of recruitment and maximal discharge of motor neurons determine the maximal rate of force development in humans, The Journal of Physiology, Vol: 597, Pages: 2445-2456, ISSN: 1469-7793
KEY POINTS: We propose and validate a method for accurately identifying the activity of populations of motor neurons during contractions at maximal rate of force development in humans. The behaviour of the motor neuron pool during rapid voluntary contractions in humans is presented. We show with this approach that the motor neuron recruitment speed and maximal motor unit discharge rate largely explains the individual ability in generating rapid force contractions. The results also indicate that the synaptic inputs received by the motor neurons before force is generated dictate human potential to generate force rapidly. This is the first characterization of the discharge behaviour of a representative sample of human motor neurons during rapid contractions. ABSTRACT: During rapid contractions, motor neurons are recruited in a short burst and begin to discharge at high frequencies (up to >200 Hz). In the present study, we investigated the behaviour of relatively large populations of motor neurons during rapid (explosive) contractions in humans, applying a new approach to accurately identify motor neuron activity simultaneous to measuring the rate of force development. The activity of spinal motor neurons was assessed by high-density electromyographic decomposition from the tibialis anterior muscle of 20 men during isometric explosive contractions. The speed of motor neuron recruitment and the instantaneous motor unit discharge rate were analysed as a function of the impulse (the time-force integral) and the maximal rate of force development. The peak of motor unit discharge rate occurred before force generation and discharge rates decreased thereafter. The maximal motor unit discharge rate was associated with the explosive force variables, at the whole population level (r2 = 0.71 ± 0.12; P < 0.001). Moreover, the peak motor unit discharge and maximal rate of force variables were correlated with an estimate of the suprasp
Kapelner T, Vujaklija I, Jiang N, et al., 2019, Predicting wrist kinematics from motor unit discharge timings for the control of active prostheses., J Neuroeng Rehabil, Vol: 16
BACKGROUND: Current myoelectric control algorithms for active prostheses map time- and frequency-domain features of the interference EMG signal into prosthesis commands. With this approach, only a fraction of the available information content of the EMG is used and the resulting control fails to satisfy the majority of users. In this study, we predict joint angles of the three degrees of freedom of the wrist from motor unit discharge timings identified by decomposition of high-density surface EMG. METHODS: We recorded wrist kinematics and high-density surface EMG signals from six able-bodied individuals and one patient with limb deficiency while they performed movements of three degrees of freedom of the wrist at three different speeds. We compared the performance of linear regression to predict the observed individual wrist joint angles from, either traditional time domain features of the interference EMG or from motor unit discharge timings (which we termed neural features) obtained by EMG decomposition. In addition, we propose and test a simple model-based dimensionality reduction, based on the physiological notion that the discharge timings of motor units are partly correlated. RESULTS: The regression approach using neural features outperformed regression on classic global EMG features (average R2 for neural features 0.77 and 0.64, for able-bodied subjects and patients, respectively; for time-domain features 0.70 and 0.52). CONCLUSIONS: These results indicate that the use of neural information extracted from EMG decomposition can advance man-machine interfacing for prosthesis control.
Farina D, Aszmann O, 2019, Bionic limbs: clinical reality and academic promises., Sci Transl Med, Vol: 6, Pages: 257ps12-257ps12
Three recent articles in Science Translational Medicine (Tan et al. and Ortiz-Catalan et al., this issue; Raspopovic et al., 5 Feb 2014 issue, 222ra19) present neuroprosthetic systems in which sensory information is delivered through direct nerve stimulation while controlling an action of the prosthesis--in all three cases, arm and hand movement. We discuss such sensory-motor integration and other key issues in prosthetic reconstruction, with an emphasis on the gap existing between clinically available systems and more advanced, custom-designed academic systems. In the near future, osseointegration, implanted muscle, and nerve electrodes for decoding and stimulation may be components of prosthetic systems for clinical use, available to a large patient population.
Pereira HM, Schlinder-DeLap B, Keenan KG, et al., 2019, Oscillations in neural drive and age-related reductions in force steadiness with a cognitive challenge., J Appl Physiol (1985), Vol: 126, Pages: 1056-1065
A cognitive challenge when imposed during a low-force isometric contraction will exacerbate sex- and age-related decreases in force steadiness, but the mechanism is not known. We determined the role of oscillations in the common synaptic input to motor units on force steadiness during a muscle contraction with a concurrent cognitive challenge. Forty-nine young adults (19-30 yr; 25 women, 24 men) and 36 old adults (60-85 yr; 19 women, 17 men) performed a cognitive challenge (counting backward by 13) during an isometric elbow flexion task at 5% of maximal voluntary contraction. Single-motor units were decomposed from high-density surface EMG recordings. For a subgroup of participants, motor units were matched during control and cognitive challenge trials, so the same motor unit was analyzed across conditions. Reduced force steadiness was associated with greater oscillations in the synaptic input to motor units during both control and cognitive challenge trials ( r = 0.45-0.47, P < 0.01). Old adults and young women showed greater oscillations in the common synaptic input to motor units and decreased force steadiness when the cognitive challenge was imposed, but young men showed no change across conditions (session × age × sex, P < 0.05). Oscillations in the common synaptic input to motor units is a potential mechanism for altered force steadiness when a cognitive challenge is imposed during low-force contractions in young women and old adults. NEW & NOTEWORTHY We found that oscillations in the common synaptic input to motor units were associated with a reduction in force steadiness when a cognitive challenge was imposed during low-force contractions of the elbow flexor muscles in young women and old men and women but not young men. Age- and sex-related muscle weakness was associated with these changes.
Barsotti M, Dupan S, Vujaklija I, et al., 2019, Online Finger Control Using High-Density EMG and Minimal Training Data for Robotic Applications, IEEE Robotics and Automation Letters, Vol: 4, Pages: 217-223
© 2018 IEEE. A hand impairment can have a profound impact on the quality of life. This has motivated the development of dexterous prosthetic and orthotic devices. However, their control with neuromuscular interfacing remains challenging. Moreover, existing myocontrol interfaces typically require an extensive calibration. We propose a minimally supervised, online myocontrol system for proportional and simultaneous finger force estimation based on ridge regression using only individual finger tasks for training. We compare the performance of this system when using two feature sets extracted from high-density electromyography (EMG) recordings: EMG linear envelope (ENV) and non-linear EMG to muscle activation mapping (ACT). Eight intact-limb participants were tested using online target reaching tasks. On average, the subjects hit 85% ± 9% and 91% ± 11% of single finger targets with ENV and ACT features, respectively. The hit rate for combined finger targets decreased to 29% ± 16% (ENV) and 53% ± 23% (ACT). The non-linear transformation (ACT) therefore improved the performance, leading to higher completion rate and more stable control, especially for the non-trained movement classes (better generalization). These results demonstrate the feasibility of proportional multiple finger control in intact subjects by regression on non-linear EMG features with a minimal training set of single finger tasks.
Chen C, Chai G, Guo W, et al., 2019, Prediction of finger kinematics from discharge timings of motor units: implications for intuitive control of myoelectric prostheses, JOURNAL OF NEURAL ENGINEERING, Vol: 16, ISSN: 1741-2560
Del Vecchio A, Casola A, Negro F, et al., 2019, The increase in muscle force after 4 weeks of strength training is mediated by adaptations in motor unit recruitment and rate coding, The Journal of Physiology, Vol: 597, Pages: 1873-1887, ISSN: 1469-7793
The strength of a muscle typically begins to increase after only a few sessions of strength training. This increase is usually attributed to changes in the neural drive to muscle as a result of adaptations at the cortical or spinal level. We investigated the change in the discharge characteristics of large populations of longitudinally tracked motor units in tibialis anterior before and after 4 weeks of strength training the ankle‐dorsiflexor muscles with isometric contractions. The adaptations exhibited by 14 individuals were compared with 14 control subjects. High‐density electromyogram grids with 128 electrodes recorded the myoelectric activity during isometric ramp contractions to the target forces of 35%, 50% and 70% of maximal voluntary force. The motor unit recruitment and derecruitment thresholds, discharge rate, interspike intervals and estimates of synaptic inputs to motor neurons were assessed. The normalized recruitment‐threshold forces of the motor units were decreased after strength training (P < 0.05). Moreover, discharge rate increased by 3.3 ± 2.5 pps (average across subjects and motor units) during the plateau phase of the submaximal isometric contractions (P < 0.001). Discharge rates at recruitment and derecruitment were not modified by training (P < 0.05). The association between force and motor unit discharge rate during the ramp‐phase of the contractions was also not altered by training (P < 0.05). These results demonstrate for the first time that the increase in muscle force after 4 weeks of strength training is the result of an increase in motor neuron output from the spinal cord to the muscle.
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