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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. [ABSTRACT FROM PUBLISHER]

Background: Electromyography (EMG) pattern-recognition based control strategies for multifunctional myoelectric prosthesis systems have been studied commonly in a controlled laboratory setting. Before these myoelectric prosthesis systems are clinically viable, it will be necessary to assess the effect of some disparities between the ideal laboratory setting and practical use on the control performance. One important obstacle is the impact of arm position variation that causes the changes of EMG pattern when performing identical motions in different arm positions. This study aimed to investigate the impacts of arm position variation on EMG pattern-recognition based motion classification in upper-limb amputees and the solutions for reducing these impacts. Methods: With five unilateral transradial (TR) amputees, the EMG signals and tri-axial accelerometer mechanomyography (ACC-MMG) signals were simultaneously collected from both amputated and intact arms when performing six classes of arm and hand movements in each of five arm positions that were considered in the study. The effect of the arm position changes was estimated in terms of motion classification error and compared between amputated and intact arms. Then the performance of three proposed methods in attenuating the impact of arm positions was evaluated. Results: With EMG signals, the average intra-position and inter-position classification errors across all five arm positions and five subjects were around 7.3% and 29.9% from amputated arms, respectively, about 1.0% and 10% low in comparison with those from intact arms. While ACC-MMG signals could yield a similar intra-position classification error (9.9%) as EMG, they had much higher inter-position classification error with an average value of 81.1% over the arm positions and the subjects. When the EMG data from all five arm positions were involved in the training set, the average classification error reached a value of around 10.8% for amputated arms. Using a two-stage cascade classifier, the average classification error was around 9.0% over all five arm positions. Reducing ACC-MMG channels from 8 to 2 only increased the average position classification error across all five arm positions from 0.7% to 1.0% in amputated arms. Conclusions: The performance of EMG pattern-recognition based method in classifying movements strongly depends on arm positions. This dependency is a little stronger in intact arm than in amputated arm, which suggests that the investigations associated with practical use of a myoelectric prosthesis should use the limb amputees as subjects instead of using able-body subjects. The two-stage cascade classifier mode with ACC-MMG for limb position identification and EMG for limb motion classification may be a promising way to reduce the effect of limb position variation on classification performance. [ABSTRACT FROM AUTHOR]

Targeted muscle reinnervation has been introduced as an effective neural machine interface. In the case of a shoulder disarticulation patient, an effective site for a nerve transfer involves the pectoralis muscles, as these perform little useful function with a missing limb. Consequently, the myoelectric signals measured from the reinnervated muscles may be corrupted by a large amount of ECG interference. This paper investigates the effect of ECG upon the accuracy of a pattern-classification-based scheme for myoelectric control of powered upper limb prostheses. The results suggest that ECG interference, at levels typically encountered in a clinical measurement, has little effect upon classification accuracy, but can affect the estimate of myoelectric activity used to convey the velocity of motion (commonly referred to as proportional control). High-pass filtering at approximately 100 Hz appears to effectively mitigate the effect of ECG interference. [ABSTRACT FROM AUTHOR]

This study introduces the application of nonlinear spatial filters to help identify single motor unit discharge from multiple channel surface electromyogram (EMG) signals during low force contractions. The nonlinear spatial filters simultaneously take into account the instantaneous amplitude and frequency information of a signal. This property was used to enhance motor unit action potentials (MUAPs) in the surface EMG record. The advantages of nonlinear spatial filtering for surface MUAP enhancement were investigated using both simulation and experimental approaches. The simulation results indicate that when compared with various linear spatial filters, nonlinear spatial filtering achieved higher SNR and higher kurtosis of the surface EMG distribution. Over a broad range of SNR and kurtosis levels for the input signal, nonlinear spatial filters achieved at least 32 times greater SNR and 11% higher kurtosis for correlated noise, and at least 15 times greater SNR and 1.7 times higher kurtosis for independent noise, across electrode array channels. The improvements offered by nonlinear spatial filters were further documented by applying them to experimental surface EMG array recordings. Compared with linear spatial filters, nonlinear spatial filters achieved at least nine times greater SNR and 25% higher kurtosis. It follows that nonlinear spatial filters represent a potentially useful supplement to linear spatial filters for detection of motor unit activity in surface EMG at low force contractions. [ABSTRACT FROM AUTHOR]

An analysis of the motor control information content made available with a neural–machine interface (NMI) in four subjects is presented in this study. We have developed a novel NMI–called targeted muscle reinnervation (TMR)—to improve the function of artificial arms for amputees. TMR involves transferring the residual amputated nerves to nonfunctional muscles in amputees. The reinnervated muscles act as biological amplifiers of motor commands in the amputated nerves and the surface electromyogram (EMG) can be used to enhance control of a robotic arm. Although initial clinical success with TMR has been promising, the number of degrees of freedom of the robotic arm that can be controlled has been limited by the number of reinnervated muscle sites. In this study we assess how much control information can be extracted from reinnervated muscles using high-density surface EMG electrode arrays to record surface EMG signals over the reinnervated muscles. We then applied pattern classification techniques to the surface EMG signals. High accuracy was achieved in the classification of 16 intended arm, hand, and finger/thumb movements. Preliminary analyses of the required number of EMG channels and computational demands demonstrate clinical feasibility of these methods. This study indicates that TMR combined with pattern-recognition techniques has the potential to further improve the function of prosthetic limbs. In addition, the results demonstrate that the central motor control system is capable of eliciting complex efferent commands for a missing limb, in the absence of peripheral feedback and without retraining of the pathways involved. [ABSTRACT FROM AUTHOR]

Objective: The contribution of the contralesional motor cortex to the impaired limbs is still controversial. The aim of this study was to investigate the role of descending projections from the contralesional hemisphere during voluntary elbow flexion on the paretic side. Design: Eleven healthy and 10 stroke subjects performed unilateral isometric elbow flexion tasks at various submaximal levels. Transcranialmagnetic stimulation was delivered to the hotspot of bicepsmuscles ipsilateral to the target side (paretic side in stroke subjects or right side in controls) at rest and during elbow flexion tasks. Motor-evoked potential amplitudes of the contralateral resting biceps muscles, transcranial magnetic stimulation--induced ipsilateral force increment, and reflex torque and weakness of spastic elbow flexors were quantified. Results: The normalized motor-evoked potential amplitude increased with force level in both healthy and stroke subjects. However, stroke subjects exhibited significantly higher force increment compared with healthy subjects only at low level of elbow flexion but similar at moderate to high levels. The greater force increment significantly correlated with reflex torque of the spastic elbow flexors, but not weakness. Conclusions: These results provide novel evidence that ipsilateral projections are not likely to contribute to strength but are correlated to spasticity of spastic-paretic elbow flexors after stroke. [ABSTRACT FROM AUTHOR]

A 51-year-old man with an incomplete C6 spinal cord injury sustained 26 yrs ago attended twenty 2-hr visits over 10 wks for robot-assisted hand training driven by myoelectric pattern recognition. In each visit, his right hand was assisted to perform motions by an exoskeleton robot, while the robot was triggered by his own motion intentions. The hand robot was designed for this study, which can perform six kinds of motions, including hand closing/opening; thumb, index finger, and middle finger closing/opening; and middle, ring, and little fingers closing/opening. After the training, his grip force increased from 13.5 to 19.6 kg, his pinch force remained the same (5.0 kg), his score of Box and Block test increased from 32 to 39, and his score from the Graded Redefined Assessment of Strength, Sensibility, and Prehension test Part 4.B increased from 22 to 24. He accomplished the tasks in the Graded Redefined Assessment of Strength, Sensibility, and Prehension test Part 4.B 28.8% faster on average. The results demonstrate the feasibility and effectiveness of robot-assisted training driven by myoelectric pattern recognition after spinal cord injury. [ABSTRACT FROM AUTHOR]

Objectives: Botulinum toxin injection remains the first line management for focal spasticity in stroke patients. Botulinum toxin blocks presynaptic release of acetylcholine at the neuromuscular junction. Current knowledge of the innervation zone (IZ) of motor points is based on histological cadaver studies. The specific aim was to localize IZ non-invasively using high-density EMG recordings in healthy (Exp 1) and stroke (Exp 2) subjects. Design: We enrolled 11 young and healthy subjects in Exp 1 and 10 hemiparetic stroke subjects in Exp 2. The subjects had similar procedures in Exp 1 and 2. A high-density linear electrode array was placed from the proximal to distal tendon junction of biceps brachii muscle. EMG recordings were made on each side in a randomized order during the following two conditions: 1) maximum voluntary contraction (MVC) of elbow flexion and 2) electrical stimulation of the musculocutenous nerve at the intensity that generated maximum responses (M-wave) using an electrical stimulator. Stimulus artifacts were removed from EMG recordings offline. The IZ was localized where the polarity of motor unit action potentials changes, i.e., cross-correlation between adjacent two bipolar signals is the lowest. Results: Innervation zones were easily detected visually and were confirmed by cross-correlation analysis in both MVC and M-wave conditions for both healthy and stroke subjects. There were no differences in IZ locations between two sides in healthy and stroke subjects, and between MVC and M-wave methods. Conclusions: This study demonstrated successful and reliable localization of IZ of biceps muscles in both healthy and stroke subjects. The findings suggest that the M-wave technique could be applied to patients with severe spasticity to detect IZ. These patients usually can not produce voluntary contractions. High-density EMG recordings with M-wave technique could be used for accurate IZ location, which could guide botulinum toxin injection for maximum outcomes. [ABSTRACT FROM AUTHOR]

Background: Multi-channel recording of surface electromyographyic (EMG) signals is very likely to be contaminated by electrocardiographic (ECG) interference, specifically when the surface electrode is placed on muscles close to the heart.Methods: A novel fast independent component analysis (FastICA) based peel-off method is presented to remove ECG interference contaminating multi-channel surface EMG signals. Although demonstrating spatial variability in waveform shape, the ECG interference in different channels shares the same firing instants. Utilizing the firing information estimated from FastICA, ECG interference can be separated from surface EMG by a "peel off" processing. The performance of the method was quantified with synthetic signals by combining a series of experimentally recorded "clean" surface EMG and "pure" ECG interference.Results: It was demonstrated that the new method can remove ECG interference efficiently with little distortion to surface EMG amplitude and frequency. The proposed method was also validated using experimental surface EMG signals contaminated by ECG interference.Conclusions: The proposed FastICA peel-off method can be used as a new and practical solution to eliminating ECG interference from multichannel EMG recordings. [ABSTRACT FROM AUTHOR]

Electromyographic (EMG) signals from muscles in the body torso are often contaminated by electrocardiography (ECG) interferences, which consequently compromise EMG intensity estimation. The ECG interference has become a barrier to proportional control of myoelectric prosthesis using a neural machine interface called targeted muscle reinnervation (TMR), which involves transferring the residual amputated nerves to nonfunctional muscles (typically pectoralis muscles for high level amputations). This study investigates a novel approach toward implementation of proportional myoelectric control by applying sample entropy (SampEn) analysis of surface EMG signals for robust intensity estimation in the presence of significant ECG interference. Surface EMG data from able-bodied and TMR amputee subjects with different degrees of ECG interference were used for performance evaluation. The results showed that the SampEn analysis had high correlation with surface EMG amplitude measurement but low sensitivity to different degrees of ECG interference. Taking this advantage, SampEn analysis of surface EMG signal can be used to facilitate implementation of proportional myoelectric control against ECG interference. This is particularly important for TMR prosthesis users. [ABSTRACT FROM AUTHOR]

Background: High density surface electromyogram (EMG) techniques with electrode arrays have been used to record spontaneous muscle activity, which is important, both for supporting the diagnosis of neuromuscular diseases and for laboratory based neurophysiological investigations. This short report addresses a practical issue we have experienced during recording of spontaneous muscle activity using electrode arrays from subjects with major neuromuscular disorders. Findings: We show that recording artifacts can appear similar to spontaneous action potential spikes. Moreover, a causal filter may induce asymmetric distortions of an artifact and thus confuse it with a real action potential spike. As a consequence, for a single channel surface EMG recording, it might be difficult to judge whether a voltage transient is a real action potential or an artifact. Further investigation of the signal distributions among other channels of the array can be used to reach a more confident judgment. Conclusions: During examination of spontaneous muscle activity using electrode arrays, caution is required for differentiation of physiological signals from artifactual spikes, which is important for accurate extraction of diagnostic or investigatory information. [ABSTRACT FROM AUTHOR]

Background: High density surface electromyogram (EMG) techniques with electrode arrays have been used to record spontaneous muscle activity, which is important, both for supporting the diagnosis of neuromuscular diseases and for laboratory based neurophysiological investigations. This short report addresses a practical issue we have experienced during recording of spontaneous muscle activity using electrode arrays from subjects with major neuromuscular disorders. Findings: We show that recording artifacts can appear similar to spontaneous action potential spikes. Moreover, a causal filter may induce asymmetric distortions of an artifact and thus confuse it with a real action potential spike. As a consequence, for a single channel surface EMG recording, it might be difficult to judge whether a voltage transient is a real action potential or an artifact. Further investigation of the signal distributions among other channels of the array can be used to reach a more confident judgment. Conclusions: During examination of spontaneous muscle activity using electrode arrays, caution is required for differentiation of physiological signals from artifactual spikes, which is important for accurate extraction of diagnostic or investigatory information. [ABSTRACT FROM AUTHOR]

Background High density surface electromyogram (EMG) techniques with electrode arrays have been used to record spontaneous muscle activity, which is important, both for supporting the diagnosis of neuromuscular diseases and for laboratory based neurophysiological investigations. This short report addresses a practical issue we have experienced during recording of spontaneous muscle activity using electrode arrays from subjects with major neuromuscular disorders. Findings We show that recording artifacts can appear similar to spontaneous action potential spikes. Moreover, a causal filter may induce asymmetric distortions of an artifact and thus confuse it with a real action potential spike. As a consequence, for a single channel surface EMG recording, it might be difficult to judge whether a voltage transient is a real action potential or an artifact. Further investigation of the signal distributions among other channels of the array can be used to reach a more confident judgment. Conclusions During examination of spontaneous muscle activity using electrode arrays, caution is required for differentiation of physiological signals from artifactual spikes, which is important for accurate extraction of diagnostic or investigatory information. [ABSTRACT FROM AUTHOR]

In this study, we used muscle and motor unit indices, derived from convenient surface electromyography (EMG) measurements, for examination of paretic muscle changes post stroke. For 12 stroke subjects, compound muscle action potential and voluntary surface EMG signals were recorded from paretic and contralateral first dorsal interosseous, abductor pollicis brevis, and abductor digiti minimi muscles. Muscle activation index (AI), motor unit number index (MUNIX), and motor unit size index (MUSIX) were then calculated for each muscle. There was a significant AI reduction for all the three muscles in paretic side compared with contralateral side, providing an evidence of muscle activation deficiency after stroke. The hand MUNIX (defined by summing the values from the three muscles) was significantly reduced in paretic side compared with contralateral side, whereas the hand MUSIX was not significantly different. Furthermore, diverse changes in MUNIX and MUSIX were observed from the three muscles. A major feature of the present examinations is the primary reliance on surface EMG, which offers practical benefits because it is noninvasive, induces minimal discomfort and can be performed quickly. [ABSTRACT FROM PUBLISHER]

This study investigates an electromyogram (EMG)-based neural interface toward hand rehabilitation for patients with cerebral palsy (CP). Forty-eight channels of surface EMG signals were recorded from the forearm of eight adult subjects with CP, while they tried to perform six different hand grasp patterns. A series of myoelectric pattern recognition analyses were performed to identify the movement intention of each subject with different EMG feature sets and classifiers. Our results indicate that across all subjects high accuracies (average overall classification accuracy > 98%) can be achieved in classification of six different hand movements, suggesting that there is substantial motor control information contained in paretic muscles of the CP subjects. Furthermore, with a feature selection analysis, it was found that a small number of ranked EMG features can maintain high classification accuracies comparable to those obtained using all the EMG features (average overall classification accuracy > 96% with 16 selected EMG features). The findings of the study suggest that myoelectric pattern recognition may be a useful control strategy for promoting hand rehabilitation in CP patients. [ABSTRACT FROM AUTHOR]

Objectives: Weakness after stroke is widely seen, and it often has adverse functional sequelae. Peripheral factors such as muscle fiber loss, fat infiltration, altered contractile properties, and an inability to fully activate the muscles on the impaired side are all contributing mechanisms. To further understand peripheral and central contributions, we used M-wave EMG recordings to reflect peripheral neuromuscular capabilities, and EMG recordings during maximal voluntary contraction (MVC) to reveal maximal voluntary activation. The ratio of MVC EMG and M-wave EMG provides an estimate of voluntary activation level. Accordingly, our specific aims were 1) to compare peripheral neuromuscular capabilities (M-wave EMG), maximal voluntary activation (MVC EMG), and voluntary activation level (the ratio) of the biceps brachii muscle between impaired and non-impaired side, 2) to correlate voluntary activation level with weakness. Design: Nine chronic hemiparetic stroke subjects (6 male, 3 female; mean: 62.7 years of age; months after stroke: 45.3, ranging from 28 to 93; MAS 0, 1, and 1+) participated in the experiment. Subjects were seated comfortably on a height- adjustable chair with the arm secured firmly on a customized apparatus. A linear electrode array of 20 bars was placed on the biceps brachii muscle for EMG recordings. Two conditions were tested on each side: 1) maximum voluntary contraction (MVC) of elbow flexion and 2) electrical stimulation of the musculocutaneous nerve at the intensity that generated maximum responses (M-wave). Torque during MVC tasks was recorded. The following variables were calculated: EMGM-wave, EMGMVC, peak torque (TorqueMVC), EMGMVC/M-wave on each side. To normalize individual absolute values, the ratios of EMGM-wave/TorqueMVC and EMGMVC/TorqueMVC were also computed. Weakness in defined as the ratio of TorqueMVC on the impaired side to the non-impaired side. Results: As expected, peak torque was significantly smaller on the impaired than non-impaired side (27.9 Nm vs. 44.5Nm, p=0.04). EMGM-wave was also significantly smaller on the impaired than non-impaired side (1743.7 uV vs. 3269.2 uV, pG0.0002). However, normalized EMGM-wave/TorqueMVC was not significantly different between two sides (85.2 vs. 90.0 uV/Nm, p=0.81). The MVC EMG data showed a different pattern. EMGMVC was smaller on the impaired than non-impaired side (588.0 uV vs. 1410.9 uV, p=0.01). Normalized EMGM-wave/TorqueMVC showed the same (19.6 vs. 32.1 uV/Nm, p=0.04). The voluntary activation level, EMGMVC/M-wave, was also smaller on the impaired than non-impaired side (0.27 vs. 0.40, p=0.08). Using linear regression analysis, the voluntary activation level on the impaired side was highly correlated with weakness (R=0.72), but very low (R=0.32) on non-impaired side. Conclusions: Collectively, our findings suggest that both peripheral and central factors contribute to post-stroke weakness, but activation deficit correlates most closely with weakness. These findings also provide further evidence to highlight the potential benefit from high-intensity exercises to enhance central activation for facilitation of motor recovery. [ABSTRACT FROM AUTHOR]