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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]

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]

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]