Mimicking muscle activity with electrical stimulation

An electrical stimulation intervention protocol to prevent disuse atrophy and muscle strength decline: an experimental study in rat

BACKGROUND

Skeletal muscle is negatively impacted by conditions such as spaceflight or prolonged bed rest, resulting in a dramatic decline in muscle mass, maximum contractile force, and muscular endurance. Electrical stimulation (ES) is an essential tool in neurophysiotherapy and an effective means of preventing skeletal muscle atrophy and dysfunction. Historically, ES treatment protocols have used either low or high frequency electrical stimulation (LFES/HFES). However, our study tests the use of a combination of different frequencies in a single electrical stimulation intervention in order to determine a more effective protocol for improving both skeletal muscle strength and endurance.

METHODS

An adult male SD rat model of muscle atrophy was established through 4 weeks of tail suspension (TS). To investigate the effects of different frequency combinations, the experimental animals were treated with low (20 Hz) or high (100 Hz) frequency before TS for 6 weeks, and during TS for 4weeks. The maximum contraction force and fatigue resistance of skeletal muscle were then assessed before the animals were sacrificed. The muscle mass, fiber cross-sectional area (CSA), fiber type and related protein expression were examined and analyzed to gain insights into the mechanisms by which the ES intervention protocol used in this study regulates muscle strength and endurance.

RESULTS

After 4 weeks of unloading, the soleus muscle mass and fiber CSA decreased by 39% and 58% respectively, while the number of glycolytic muscle fibers increased by 21%. The gastrocnemius muscle fibers showed a 51% decrease in CSA, with a 44% decrease in single contractility and a 39% decrease in fatigue resistance. The number of glycolytic muscle fibers in the gastrocnemius also increased by 29%. However, the application of HFES either prior to or during unloading showed an improvement in muscle mass, fiber CSA, and oxidative muscle fibers. In the pre-unloading group, the soleus muscle mass increased by 62%, while the number of oxidative muscle fibers increased by 18%. In the during unloading group, the soleus muscle mass increased by 29% and the number of oxidative muscle fibers increased by 15%. In the gastrocnemius, the pre-unloading group showed a 38% increase in single contractile force and a 19% increase in fatigue resistance, while in the during unloading group, a 21% increase in single contractile force and a 29% increase in fatigue resistance was observed, along with a 37% and 26% increase in the number of oxidative muscle fibers, respectively. The combination of HFES before unloading and LFES during unloading resulted in a significant elevation of the soleus mass by 49% and CSA by 90%, with a 40% increase in the number of oxidative muscle fibers in the gastrocnemius. This combination also resulted in a 66% increase in single contractility and a 38% increase in fatigue resistance.

CONCLUSION

Our results indicated that using HFES before unloading can reduce the harmful effects of muscle unloading on the soleus and gastrocnemius muscles. Furthermore, we found that combining HFES before unloading with LFES during unloading was more effective in preventing muscle atrophy in the soleus and preserving the contractile function of the gastrocnemius muscle.

Asynchronous axonal firing patterns evoked via continuous subthreshold kilohertz stimulation

Objective.Transcutaneous electrical stimulation of peripheral nerves is a common technique to assist or rehabilitate impaired muscle activation. However, conventional stimulation paradigms activate nerve fibers synchronously with action potentials time-locked with stimulation pulses. Such synchronous activation limits fine control of muscle force due to synchronized force twitches. Accordingly, we developed a subthreshold high-frequency stimulation waveform with the goal of activating axons asynchronously.Approach.We evaluated our waveform experimentally and through model simulations. During the experiment, we delivered continuous subthreshold pulses at frequencies of 16.67, 12.5, or 10 kHz transcutaneously to the median and ulnar nerves. We obtained high-density electromyographic (EMG) signals and fingertip forces to quantify the axonal activation patterns. We used a conventional 30 Hz stimulation waveform and the associated voluntary muscle activation for comparison. We modeled stimulation of biophysically realistic myelinated mammalian axons using a simplified volume conductor model to solve for extracellular electric potentials. We compared the firing properties under kHz and conventional 30 Hz stimulation.Main results.EMG activity evoked by kHz stimulation showed high entropy values similar to voluntary EMG activity, indicating asynchronous axon firing activity. In contrast, we observed low entropy values in EMG evoked by conventional 30 Hz stimulation. The muscle forces evoked by kHz stimulation also showed more stable force profiles across repeated trials compared with 30 Hz stimulation. Our simulation results provide direct evidence of asynchronous firing patterns across a population of axons in response to kHz frequency stimulation, while 30 Hz stimulation elicited synchronized time-locked responses across the population.Significance.We demonstrate that the continuous subthreshold high-frequency stimulation waveform can elicit asynchronous axon firing patterns, which can lead to finer control of muscle forces.

Restoration of complex movement in the paralyzed upper limb

OBJECTIVE

Functional electrical stimulation (FES) involves artificial activation of skeletal muscles to reinstate motor function in paralyzed individuals. While FES applied to the upper limb has improved the ability of tetraplegics to perform activities of daily living, there are key shortcomings impeding its widespread use. One major limitation is that the range of motor behaviors that can be generated is restricted to a small set of simple, preprogrammed movements. This limitation stems from the substantial difficulty in determining the patterns of stimulation across many muscles required to produce more complex movements. Therefore, the objective of this study was to use machine learning to flexibly identify patterns of muscle stimulation needed to evoke a wide array of multi-joint arm movements.

APPROACH

Arm kinematics and electromyographic activity from 29 muscles were recorded while a 'trainer' monkey made an extensive range of arm movements. Those data were used to train an artificial neural network that predicted patterns of muscle activity associated with a new set of movements. Those patterns were converted into trains of stimulus pulses that were delivered to upper limb muscles in two other temporarily paralyzed monkeys.

RESULTS

Machine-learning based prediction of EMG was good for within-subject predictions but appreciably poorer for across-subject predictions. Evoked responses matched the desired movements with good fidelity only in some cases. Means to mitigate errors associated with FES-evoked movements are discussed.

SIGNIFICANCE

Because the range of movements that can be produced with our approach is virtually unlimited, this system could greatly expand the repertoire of movements available to individuals with high level paralysis.

A hybrid method for real-time stimulation artefact removal during functional electrical stimulation with time-variant parameters

Objective. In this study, a hybrid method combining hardware and software architecture is proposed to remove stimulation artefacts (SAs) and extract the volitional surface electromyography (sEMG) in real time during functional electrical stimulations (FES) with time-variant parameters.Approach. First, an sEMG detection front-end (DFE) combining fast recovery, detector and stimulator isolation and blanking is developed and is capable of preventing DFE saturation with a blanking time of 7.6 ms. The fragment between the present stimulus and previous stimulus is set as an SA fragment. Second, an SA database is established to provide six high-similarity templates with the current SA fragment. The SA fragment will be de-artefacted by a 6th-order Gram-Schmidt (GS) algorithm, a template-subtracting method, using the provided templates, and this database-based GS algorithm is called DBGS. The provided templates are previously collected SA fragments with the same or a similar evoking FES intensity to that of the current SA fragment, and the lengths of the templates are longer than that of the current SA fragment. After denoising, the sEMG will be extracted, and the current SA fragment will be added to the SA database. The prototype system based on DBGS was tested on eight able-bodied volunteers and three individuals with stroke to verify its capacity for stimulation removal and sEMG extraction.Results.The average stimulus artefact attenuation factor, SA index and correlation coefficient between clean sEMG and extracted sEMG for 6th-order DBGS were 12.77 ± 0.85 dB, 1.82 ± 0.37 dB and 0.84 ± 0.33 dB, respectively, which were significantly higher than those for empirical mode decomposition combined with notch filters, pulse-triggered GS algorithm, 1st-order and 3rd-order DBGS. The sEMG-torque correlation coefficients were 0.78 ± 0.05 and 0.48 ± 0.11 for able-bodied volunteers and individuals with stroke, respectively.Significance.The proposed hybrid method can extract sEMG during dynamic FES in real time.

Functional Electrical Stimulation Controlled by Motor Imagery Brain-Computer Interface for Rehabilitation

Sensorimotor rhythm (SMR)-based brain–computer interface (BCI) controlled Functional Electrical Stimulation (FES) has gained importance in recent years for the rehabilitation of motor deficits. However, there still remain many research questions to be addressed, such as unstructured Motor Imagery (MI) training procedures; a lack of methods to classify different MI tasks in a single hand, such as grasping and opening; and difficulty in decoding voluntary MI-evoked SMRs compared to FES-driven passive-movement-evoked SMRs. To address these issues, a study that is composed of two phases was conducted to develop and validate an SMR-based BCI-FES system with 2-class MI tasks in a single hand (Phase 1), and investigate the feasibility of the system with stroke and traumatic brain injury (TBI) patients (Phase 2). The results of Phase 1 showed that the accuracy of classifying 2-class MIs (approximately 71.25%) was significantly higher than the true chance level, while that of distinguishing voluntary and passive SMRs was not. In Phase 2, where the patients performed goal-oriented tasks in a semi-asynchronous mode, the effects of the FES existence type and adaptive learning on task performance were evaluated. The results showed that adaptive learning significantly increased the accuracy, and the accuracy after applying adaptive learning under the No-FES condition (61.9%) was significantly higher than the true chance level. The outcomes of the present research would provide insight into SMR-based BCI-controlled FES systems that can connect those with motor disabilities (e.g., stroke and TBI patients) to other people by greatly improving their quality of life. Recommendations for future work with a larger sample size and kinesthetic MI were also presented.

Improved muscle activation using proximal nerve stimulation with subthreshold current pulses at kilohertz-frequency

OBJECTIVE

Transcutaneous electrical nerve stimulation can help individuals with neurological disorders to regain their motor function by activating muscles externally. However, conventional stimulation technique often induces near-simultaneous recruitment of muscle fibers, leading to twitch forces time-locked to the stimulation.

APPROACH

To induce less synchronized activation of finger flexor muscles, we delivered clustered narrower pulses to the proximal segment of the median and ulnar nerves at a carrier frequency of either 10 kHz (with an 80 µs pulse width) or 7.14 kHz (with a 120 µs pulse width) (high-frequency mode, HF), and different clustered pulses were delivered at a frequency of 30 or 40 Hz. Conventional stimulation with pulse frequency of 30 or 40 Hz (low-frequency mode, LF) was used for comparison. With matched elicited muscle forces between the HF and LF modes, the force variation, the high-density electromyogram (EMG) signals recorded at the finger flexor muscles and stimulation-induced-pain levels were compared.

MAIN RESULTS

The compound action potentials in the 10 kHz HF mode revealed a significant difference (i.e. a lower amplitude and area, and a wider duration) compared with the LF mode, indicating cancellations of asynchronized action potentials. A smaller fluctuation in the elicited forces in the 10 kHz mode further demonstrated the less synchronized activation of different motor units. These effects tended to be weaker in the 7.14 kHz HF condition. However, the levels of pain sensation was not reduced in the HF mode potentially due to the high charge density used in the HF mode. Our findings indicated that different nerve fibers were recruited asynchronously through summations of different numbers of subthreshold depolarizations in the HF mode.

SIGNIFICANCE

Compared with the LF mode, the HF mode stimulation was capable of activating the nerve fibers in a less synchronized way, which is more similar to the physiological activation pattern.

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation

Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links between living brains and artificial actuators. Although theoretical propositions and some proof of concept experiments on directly linking the brains with machines date back to the early 1960s, BMI research only took off in earnest at the end of the 1990s, when this approach became intimately linked to new neurophysiological methods for sampling large-scale brain activity. The classic goals of BMIs are 1) to unveil and utilize principles of operation and plastic properties of the distributed and dynamic circuits of the brain and 2) to create new therapies to restore mobility and sensations to severely disabled patients. Over the past decade, a wide range of BMI applications have emerged, which considerably expanded these original goals. BMI studies have shown neural control over the movements of robotic and virtual actuators that enact both upper and lower limb functions. Furthermore, BMIs have also incorporated ways to deliver sensory feedback, generated from external actuators, back to the brain. BMI research has been at the forefront of many neurophysiological discoveries, including the demonstration that, through continuous use, artificial tools can be assimilated by the primate brain's body schema. Work on BMIs has also led to the introduction of novel neurorehabilitation strategies. As a result of these efforts, long-term continuous BMI use has been recently implicated with the induction of partial neurological recovery in spinal cord injury patients.

A functional electrical stimulation system for human walking inspired by reflexive control principles

This study presents an innovative multichannel functional electrical stimulation gait-assist system which employs a well-established purely reflexive control algorithm, previously tested in a series of bipedal walking robots. In these robots, ground contact information was used to activate motors in the legs, generating a gait cycle similar to that of humans. Rather than developing a sophisticated closed-loop functional electrical stimulation control strategy for stepping, we have instead utilised our simple reflexive model where muscle activation is induced through transfer functions which translate sensory signals, predominantly ground contact information, into motor actions. The functionality of the functional electrical stimulation system was tested by analysis of the gait function of seven healthy volunteers during functional electrical stimulation-assisted treadmill walking compared to unassisted walking. The results demonstrated that the system was successful in synchronising muscle activation throughout the gait cycle and was able to promote functional hip and ankle movements. Overall, the study demonstrates the potential of human-inspired robotic systems in the design of assistive devices for bipedal walking.

Real-time and wearable functional electrical stimulation system for volitional hand motor function control using the electromyography bridge method

Voluntary participation of hemiplegic patients is crucial for functional electrical stimulation therapy. A wearable functional electrical stimulation system has been proposed for real-time volitional hand motor function control using the electromyography bridge method. Through a series of novel design concepts, including the integration of a detecting circuit and an analog-to-digital converter, a miniaturized functional electrical stimulation circuit technique, a low-power super-regeneration chip for wireless receiving, and two wearable armbands, a prototype system has been established with reduced size, power, and overall cost. Based on wrist joint torque reproduction and classification experiments performed on six healthy subjects, the optimized surface electromyography thresholds and trained logistic regression classifier parameters were statistically chosen to establish wrist and hand motion control with high accuracy. Test results showed that wrist flexion/extension, hand grasp, and finger extension could be reproduced with high accuracy and low latency. This system can build a bridge of information transmission between healthy limbs and paralyzed limbs, effectively improve voluntary participation of hemiplegic patients, and elevate efficiency of rehabilitation training.

Prediction of muscle activity during loaded movements of the upper limb

Background

Accurate prediction of electromyographic (EMG) signals associated with a variety of motor behaviors could, in theory, serve as activity templates needed to evoke movements in paralyzed individuals using functional electrical stimulation. Such predictions should encompass complex multi-joint movements and include interactions with objects in the environment.

Methods

Here we tested the ability of different artificial neural networks (ANNs) to predict EMG activities of 12 arm muscles while human subjects made free movements of the arm or grasped and moved objects of different weights and dimensions. Inputs to the trained ANNs included hand position, hand orientation, and thumb grip force.

Results

The ability of ANNs to predict EMG was equally as good for tasks involving interactions with external loads as for unloaded movements. The ANN that yielded the best predictions was a feed-forward network consisting of a single hidden layer of 30 neural elements. For this network, the average coefficient of determination (R² value) between predicted and actual EMG signals across all nine subjects and 12 muscles during movements that involved episodes of moving objects was 0.43.

Conclusion

This reasonable accuracy suggests that ANNs could be used to provide an initial estimate of the complex patterns of muscle stimulation needed to produce a wide array of movements, including those involving object interaction, in paralyzed individuals.