No abstract

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.

This study presents a novel method for detection of the onset time of muscle activity using surface electromyogram (EMG) signals. The method takes advantage of the nonlinear properties of the Teager-Kaiser energy (TKE) operator, which simultaneously considers the amplitude and instantaneous frequency of the surface EMG, and therefore increases the prospects of muscle activity detection. To detect the onset time of muscle activity, the surface EMG signal was first processed by the TKE operator to highlight motor unit activities of the muscle. Then a robust threshold-based algorithm was developed in the TKE domain to locate the onset of muscle activity. The validity of the proposed method was illustrated using various surface EMG simulations as well as experimental surface EMG recordings.

This study presents a progressive FastICA peel-off (PFP) framework for high density surface electromyogram (EMG) decomposition. The novel framework is based on a shift-invariant model for describing surface EMG. The decomposition process can be viewed as progressively expanding the set of motor unit spike trains, which is primarily based on FastICA. To overcome the local convergence of FastICA, a “peel off” strategy (i.e. removal of the estimated motor unit action potential (MUAP) trains from the previous step) is used to mitigate the effects of the already identified motor units, so more motor units can be extracted. Moreover, a constrained FastICA is applied to assess the extracted spike trains and correct possible erroneous or missed spikes. These procedures work together to improve the decomposition performance. The proposed framework was validated using simulated surface EMG signals with different motor unit numbers (30, 70, 91) and signal to noise ratios (SNRs) (20, 10, 0 dB). The results demonstrated relatively large numbers of extracted motor units and high accuracies (high F1-scores). The framework was also tested with 111 trials of 64-channel electrode array experimental surface EMG signals during the first dorsal interosseous (FDI) muscle contraction at different intensities. On average 14.1 ± 5.0 motor units were identified from each trial of experimental surface EMG signals.

Walking dysfunction occurs at a very high prevalence in stroke survivors. Human walking is a phenomenon often taken for granted, but it is mediated by complicated neural control mechanisms. The automatic process includes the brainstem descending pathways (RST and VST) and the intraspinal locomotor network. It is known that leg muscles are organized into modules to serve subtasks for body support, posture and locomotion. Major kinematic mechanisms are recognized to minimize the center of gravity (COG) displacement. Stroke leads to damage to motor cortices and their descending corticospinal tracts and subsequent muscle weakness. On the other hand, brainstem descending pathways and the intraspinal motor network are disinhibited and become hyperexcitable. Recent advances suggest that they mediate post-stroke spasticity and diffuse spastic synergistic activation. As a result of such changes, existing modules are simplified and merged, thus leading to poor body support and walking performance. The wide range and hierarchy of post-stroke hemiplegic gait impairments is a reflection of mechanical consequences of muscle weakness, spasticity, abnormal synergistic activation and their interactions. Given the role of brainstem descending pathways in body support and locomotion and post-stroke spasticity, a new perspective of understanding post-stroke hemiplegic gait is proposed. Its clinical implications for management of hemiplegic gait are discussed. Two cases are presented as clinical application examples.

Cortical and subcortical plastic reorganization occurs in the course of motor recovery after stroke. It is largely accepted that plasticity of ipsilesional motor cortex primarily contributes to recovery of motor function, while the contributions of contralesional motor cortex are not completely understood. As a result of damages to motor cortex and its descending pathways and subsequent unmasking of inhibition, there is evidence of upregulation of reticulospinal tract (RST) excitability in the contralesional side. Both animal studies and human studies with stroke survivors suggest and support the role of RST hyperexcitability in post-stroke spasticity. Findings from animal studies demonstrate the compensatory role of RST hyperexcitability in recovery of motor function. In contrast, RST hyperexcitability appears to be related more to abnormal motor synergy and disordered motor control in stroke survivors. It does not contribute to recovery of normal motor function. Recent animal studies highlight laterality dominance of corticoreticular projections. In particular, there exists upregulation of ipsilateral corticoreticular projections from contralesional premotor cortex (PM) and supplementary motor area (SMA) to medial reticular nuclei. We revisit and revise the previous theoretical framework and propose a unifying account. This account highlights the importance of ipsilateral PM/SMA-cortico-reticulospinal tract hyperexcitability from the contralesional motor cortex as a result of disinhibition after stroke. This account provides a pathophysiological basis for post-stroke spasticity and related movement impairments, such as abnormal motor synergy and disordered motor control. However, further research is needed to examine this pathway in stroke survivors to better understand its potential roles, especially in muscle strength and motor recovery. This account could provide a pathophysiological target for developing neuromodulatory interventions to manage spasticity and thus possibly to facilitate motor recovery.

Targeted muscle reinnervation (TMR) is a novel neural machine interface for improved myoelectric prosthesis control. Previous high-density (HD) surface electromyography (EMG) studies have indicated that tremendous neural control information can be extracted from the reinnervated muscles by EMG pattern recognition (PR). However, using a large number of EMG electrodes hinders clinical application of the TMR technique. This study investigated a reduced number of electrodes and the placement required to extract sufficient neural control information for accurate identification of user movement intents. An electrode selection algorithm was applied to the HD EMG recordings from each of 4 TMR amputee subjects. The results show that when using only 12 selected bipolar electrodes the average accuracy over subjects for classifying 16 movement intents was 93.0(±3.3)%, just 1.2% lower than when using the entire HD electrode complement. The locations of selected electrodes were consistent with the anatomical reinnervation sites. Additionally, a practical protocol for clinical electrode placement was developed, which does not rely on complex HD EMG experiment and analysis while maintaining a classification accuracy of 88.7±4.5%. These outcomes provide important guidelines for practical electrode placement that can promote future clinical application of TMR and EMG PR in the control of multifunctional prostheses.

Voluntary surface electromyogram (EMG) signal is sometimes contaminated by spurious background spikes of both physiological and extrinsic or accidental origins. A novel method of muscle activity onset detection against such spurious spikes was proposed in this study based primarily on the sample entropy (SampEn) analysis of the surface EMG. The method takes advantage of the nonlinear properties of the SampEn analysis to distinguish voluntary surface EMG signals from spurious background spikes in the complexity domain. To facilitate muscle activity onset detection, the SampEn analysis of surface EMG was first performed to highlight voluntary EMG activity while suppressing spurious background spikes. Then, a SampEn threshold was optimized for muscle activity onset detection. The performance of the proposed method was examined using both semi-synthetic and experimental surface EMG signals. The SampEn based methods effectively reduced the detection error induced by spurious background spikes and achieved improved performance over the methods relying on conventional amplitude thresholding or its extended version in the Teager Kaiser Energy domain.