Spectral properties of multiple myoelectric signals: New insights into the neural origin of muscle synergies

Muscle synergies are associated with intermuscular coherence and cortico-synergy coherence in an isometric upper limb task

To elucidate the underlying physiological mechanisms of muscle synergies, we investigated long-range functional connectivity by cortico-muscular (CMC), intermuscular (IMC) and cortico-synergy (CSC) coherence. Fourteen healthy participants executed an isometric upper limb task in synergy-tuned directions. Cortical activity was recorded using 32-channel electroencephalography (EEG) and muscle activity using 16-channel electromyography (EMG). Using non-negative matrix factorisation (NMF), we calculated muscle synergies from two different tasks. A preliminary multidirectional task was used to identify synergy-preferred directions (PDs). A subsequent coherence task, consisting of generating forces isometrically in the synergy PDs, was used to assess the functional connectivity properties of synergies. Overall, we were able to identify four different synergies from the multidirectional task. A significant alpha band IMC was consistently present in all extracted synergies. Moreover, IMC alpha band was higher between muscles with higher weights within a synergy. Interestingly, CSC alpha band was also significantly higher across muscles with higher weights within a synergy. In contrast, no significant CMC was found between the motor cortex area and synergy muscles. The presence of a shared input onto synergistic muscles within a synergy supports the idea of neurally derived muscle synergies that build human movement. Our findings suggest cortical modulation of some of the synergies and the consequential existence of shared input between muscles within cortically modulated synergies.

A pre-market interventional, single-arm clinical investigation of a new topical lotion based on hyaluronic acid and peptides, EGYFILTM, for the treatment of pain and stiffness in soft tissues

BACKGROUND

Muscle pain and stiffness are strictly interconnected. Injuries frequently occur during sport activities, causing muscle pain, with or without stiffness, and require effective as well as fast-acting treatments. Topical products can be ideal for the treatment of such physical alterations as they are convenient and simple to use. In this study, it was investigated the application of a novel topical formulation, EGYFIL™, for the treatment of pain and stiffness due to muscle contracture, trauma, and/or overtraining. The lotion is composed of hyaluronic acid, a well-known ingredient for the pain alleviation, mixed with skin conditioning SH-Polypeptide-6 and SH-Oligopeptide-1, embedded in it.

METHODS

Twenty-six patients with pain and/or stiffness were enrolled. After a screening visit (Time 0, t0), patients were treated for the first time with the IP. The treatment consisted of topical application of the pain lotion. Level of pain and stiffness were measured with Numerical Rating Scale (NRS). Patients' pain and/or stiffness were evaluated at t0 (prior to using the product), after three hours (t1), and after three days (t2) of treatment. Participants were free to apply and re-apply the product ad libitum over the course of the study period (3 days). Potential adverse events (AE) and tolerance were evaluated during each visit.

RESULTS

There was a 22% decrease in pain in the first three hours (p < 0.001), followed by an additional 20% decrease after three days (p=0.0873). Overall, there was a 42% decrease in pain over the three days of the study (p =0.001). Furthermore, a 24% reduction in stiffness in the first three hours (p=0.025) and a 38% decrease in stiffness over three days (p < 0.001) were observed. Reduction in pain and stiffness were neither age, nor sex dependent. No adverse effects were reported during the study.

CONCLUSION

EGYFIL™ is safe and seems to reduce pain and stiffness in patients during the 3 days of treatment, already after 3 h from the first application.

TRIAL REGISTRATION

ClinicalTrials.gov ID: NCT05711953. This trial was registered on 03/02/2023.

Classification of gait phases based on a machine learning approach using muscle synergy

The accurate detection of the gait phase is crucial for monitoring and diagnosing neurological and musculoskeletal disorders and for the precise control of lower limb assistive devices. In studying locomotion mode identification and rehabilitation of neurological disorders, the concept of modular organization, which involves the co-activation of muscle groups to generate various motor behaviors, has proven to be useful. This study aimed to investigate whether muscle synergy features could provide a more accurate and robust classification of gait events compared to traditional features such as time-domain and wavelet features. For this purpose, eight healthy individuals participated in this study, and wireless electromyography sensors were attached to four muscles in each lower extremity to measure electromyography (EMG) signals during walking. EMG signals were segmented and labeled as 2-class (stance and swing) and 3-class (weight acceptance, single limb support, and limb advancement) gait phases. Non-negative matrix factorization (NNMF) was used to identify specific muscle groups that contribute to gait and to provide an analysis of the functional organization of the movement system. Gait phases were classified using four different machine learning algorithms: decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and neural network (NN). The results showed that the muscle synergy features had a better classification accuracy than the other EMG features. This finding supported the hypothesis that muscle synergy enables accurate gait phase classification. Overall, the study presents a novel approach to gait analysis and highlights the potential of muscle synergy as a tool for gait phase detection.

Age-related differences in calf muscle recruitment strategies in the time-frequency domain during walking as a function of task demand

Activation of the plantar flexors is critical in governing ankle push-off power during walking, which decreases due to age. However, electromyographic (EMG) signal amplitude alone is unable to fully characterize motor unit recruitment during functional activity. Although not yet studied in walking, EMG frequency content may also vary due to age-related differences in muscle morphology and neural signaling. Our purpose was to quantify plantar flexor activation differences in the time-frequency domain between young and older adults during walking across a range of speeds and with and without horizontal aiding and impeding forces. Ten healthy young (24.0 ± 3.4 yr) and older adults (73.7 ± 3.9 yr) walked at three speeds and walked with horizontal aiding and impeding force while muscle activations of soleus (SOL) and gastrocnemius (GAS) were recorded. The EMG signals were decomposed in the time-frequency domain with wavelet transformation. Principal component analyses extracted principal components (PCs) and PC scores. Compared with young adults, we observed that GAS activation in older adults: 1) was lower across all frequency ranges during midstance and in slow to middle frequency ranges during push-off, independent of walking speed and 2) shifted to slower frequencies with earlier timing as walking speed increased. Our results implicate GAS time-frequency content, and its morphological and neural origins, as a potential determinant of hallmark ankle push-off deficits due to aging, particularly at faster walking speeds. Rehabilitation specialists may attempt to restore GAS intensity across all frequency ranges during mid-to-late stance while avoiding disproportionate increases in slower frequencies during early stance.NEW & NOTEWORTHY We use time-frequency analyses of calf muscle activation to quantify age-related differences in motor recruitment in walking. Gastrocnemius activation in older versus young adults was lower across all frequencies during midstance and in slow-to-middle frequencies during push-off, independent of speed, and shifted to slower frequencies with earlier timing as speed increased. Our results implicate gastrocnemius time-frequency content as a potential determinant of hallmark ankle push-off deficits due to aging, particularly at faster speeds.

Evaluation of Three Machine Learning Algorithms for the Automatic Classification of EMG Patterns in Gait Disorders

Gait disorders are common in neurodegenerative diseases and distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge even for the experienced clinician. Ultimately, muscle activity underlies the generation of kinematic patterns. Therefore, one possible way to address this problem may be to differentiate gait disorders by analyzing intrinsic features of muscle activations patterns. Here, we examined whether it is possible to differentiate electromyography (EMG) gait patterns of healthy subjects and patients with different gait disorders using machine learning techniques. Nineteen healthy volunteers (9 male, 10 female, age 28.2 ± 6.2 years) and 18 patients with gait disorders (10 male, 8 female, age 66.2 ± 14.7 years) resulting from different neurological diseases walked down a hallway 10 times at a convenient pace while their muscle activity was recorded via surface EMG electrodes attached to 5 muscles of each leg (10 channels in total). Gait disorders were classified as predominantly hypokinetic (n = 12) or ataxic (n = 6) gait by two experienced raters based on video recordings. Three different classification methods (Convolutional Neural Network-CNN, Support Vector Machine-SVM, K-Nearest Neighbors-KNN) were used to automatically classify EMG patterns according to the underlying gait disorder and differentiate patients and healthy participants. Using a leave-one-out approach for training and evaluating the classifiers, the automatic classification of normal and abnormal EMG patterns during gait (2 classes: "healthy" and "patient") was possible with a high degree of accuracy using CNN (accuracy 91.9%), but not SVM (accuracy 67.6%) or KNN (accuracy 48.7%). For classification of hypokinetic vs. ataxic vs. normal gait (3 classes) best results were again obtained for CNN (accuracy 83.8%) while SVM and KNN performed worse (accuracy SVM 51.4%, KNN 32.4%). These results suggest that machine learning methods are useful for distinguishing individuals with gait disorders from healthy controls and may help classification with respect to the underlying disorder even when classifiers are trained on comparably small cohorts. In our study, CNN achieved higher accuracy than SVM and KNN and may constitute a promising method for further investigation.

Dynamics of Modular Neuromotor Control of Walking and Running during Single and Dual Task Conditions

The aim of the study was to determine the stability and complexity of muscle synergies to provide insight to the neural control of gait stability in walking and running and when performing a concurrent cognitive dual task. Eighteen healthy young adults performed walking and running at preferred speeds and 120% of preferred speeds in single and dual task conditions. Muscle synergies were determined from the activity of 9 trunk and leg muscles and centre of mass (COM) motion was recorded with an inertial measurement unit. Local dynamic stability, complexity and width of synergies, and stability and complexity of COM motion were determined, in addition to the cross sample entropy to determine the coupling between COM motion and muscle synergies. Increasing locomotion speed increased complexity and decreased stability of COM motion with a concurrent decrease in synergy complexity and stability but with no change in synergy width. The coupling of COM motion and muscle synergies also increased with increasing speed. Vertical COM motion was more complex and less stable but with no change in anterior-posterior or medio-lateral directions in dual task locomotion. Muscle synergies were also more stable in dual task conditions. These findings indicate that changes in neuromotor dynamics may underpin reported changes in COM local stability during gait as the neural commands responsible for generating the movement are altered in response to increasing task demands. Increased cognitive demands lead to more stable neuromotor commands possibly to maintain local stability of COM motion in the anterior-posterior and medio-lateral directions.

How to improve the muscle synergy analysis methodology?

Muscle synergy analysis is increasingly used in domains such as neurosciences, robotics, rehabilitation or sport sciences to analyze and better understand motor coordination. The analysis uses dimensionality reduction techniques to identify regularities in spatial, temporal or spatio-temporal patterns of multiple muscle activation. Recent studies have pointed out variability in outcomes associated with the different methodological options available and there was a need to clarify several aspects of the analysis methodology. While synergy analysis appears to be a robust technique, it remain a statistical tool and is, therefore, sensitive to the amount and quality of input data (EMGs). In particular, attention should be paid to EMG amplitude normalization, baseline noise removal or EMG filtering which may diminish or increase the signal-to-noise ratio of the EMG signal and could have major effects on synergy estimates. In order to robustly identify synergies, experiments should be performed so that the groups of muscles that would potentially form a synergy are activated with a sufficient level of activity, ensuring that the synergy subspace is fully explored. The concurrent use of various synergy formulations-spatial, temporal and spatio-temporal synergies- should be encouraged. The number of synergies represents either the dimension of the spatial structure or the number of independent temporal patterns, and we observed that these two aspects are often mixed in the analysis. To select a number, criteria based on noise estimates, reliability of analysis results, or functional outcomes of the synergies provide interesting substitutes to criteria solely based on variance thresholds.

Differences in muscle synergies between healthy subjects and transfemoral amputees during normal transient-state walking speed

BACKGROUND

Lower limb amputation is a major public health issue globally, and its prevalence is increasing significantly around the world. Previous studies on lower limb amputees showed analogous complexity implemented by the neurological system which does not depend on the level of amputation.

RESEARCH QUESTION

What are the differences in muscle synergies between healthy subjects (HS) and transfemoral amputees (TFA) during self-selected normal transient-state walking speed?

METHODS

thirteen male HS and eleven male TFA participated in this study. Surface electromyography (sEMG) data were collected from HS dominant leg and TFA intact limb. Concatenated non-negative matrix factorization (CNMF) was used to extract muscle synergy components synergy vectors (S) and activation coefficient profiles (C). Correlation between a pair of synergy vectors from HS and TFA was analyzed by means of the coefficient of determination (R²). Statistical parametric mapping (SPM) was used to compare the temporal components of the muscle synergies between groups.

RESULTS

the highest correlation was perceived in synergy 2 (S2) and 3 (S3) and the lowest in synergy 1 (S1) and 4 (S4) between HS and TFA. Statistically significant differences were observed in all of the activation coefficients, particularly during the stance phase. Significant lag in the activation coefficient of S2 (due mainly to activated plantarflexors) resulted in a statistically larger portion of the gait cycle (GC) in stance phase in TFA.

SIGNIFICANCE

Understanding the activation patterns of lower limb amputees' muscles that control their intact leg (IL) and prosthetic leg (PL) joints could lead to greater knowledge of neuromuscular compensation strategies in amputees. Studying the low-dimensional muscle synergy patterns in the lower limbs can further this understanding. The findings in this study could contribute to improving gait rehabilitation of lower limb amputees and development of the new generation of prostheses.

Gait synergetic neuromuscular control in children with cerebral palsy at different gross motor function classification system levels

Cerebral palsy (CP) is a neural developmental disease featured with gait abnormalities. CP gait assessment is usually performed with the Gross Motor Function Classification System (GMFCS) in clinics, which does not involve a thorough assessment of neuromuscular control. To understand how the neuromuscular control disorders lead to gait abnormalities, we explored the relationship between GMFCS levels and the gait synergetic control characteristics in this study. In total, 18 children with CP at different GMFCS levels (mean age: 4.41±1.30 yr) and 8 age-matched typically developing (TD) children (mean age: 4.43±1.36 yr) were recruited to perform a straight walking task, and the surface electromyographic (sEMG) signals from eight lower limb muscles on each side and accelerometer data were collected. A nonnegative matrix factorization method was applied to obtain the muscle synergies from the sEMG signals. Next, synergy structures were projected onto the basic gait synergies to test the completeness of those structures. Subsequently, synergy activation parameters, including total activation duration and coactivation index, were compared across the participants. This study showed that children with CP at GMFCS levels I and II and the TD children had similar synergy structures, but the synergy activations of these children with CP were different from those of TD children. In addition, similar to previous research, we also found that children with CP at GMFCS level III could not access all four basic synergies on both sides. Based on the synergy analysis results, a gait assessment paradigm was proposed to facilitate the clinical CP gait evaluation.

NEW & NOTEWORTHY Understanding the mechanism of gait abnormality has important clinical significance for the diagnosis, prognosis, and possible treatment of motor dysfunction in children with cerebral palsy (CP). In this study, the comparisons of the lower limb muscle synergies among different groups of children with CP at different Gross Motor Function Classification System levels might provide some new insight into the mechanism underlying the gait disorder. In particular, the discrepancies of gait synergy structure and activation patterns across the study groups may indicate different neurophysiological and pathological attributes in different groups of patients.

You are better off running than walking revisited: Does an acute vestibular imbalance affect muscle synergies?

It has been suggested that vestibular cues are inhibited for the benefit of spinal locomotor centres in parallel with the increase in locomotion speed. This study aimed at quantifying the influence of a transient vestibular tone imbalance (TVTI) on gait kinematics, muscle activity and muscle synergies during walking and running. Twelve participants walk or run at a self-selected speed with or without TVTI, which was generated by 10 body rotations just prior the locomotion task. Three-dimensional lower-limb kinematic was recorded simultaneously with the surface electromyographic (EMG) activity of 8 muscles to extract muscle synergies via non-negative matrix factorization. Under TVTI, there was an increased gait deviation in walking compared to running (22.8 ± 8.4° and 8.5 ± 3.6°, respectively; p < 0.01), while the number (n = 4) and the composition of the muscle synergies did not differ across conditions (p = 0.78). A higher increase (p < 0.05) in EMG activity due to TVTI was found during walking compared to running, especially during stance. These findings confirmed that the central nervous system inhibited misleading vestibular signals according to the increase in locomotion speed for the benefit of spinal mechanisms, expressed by the muscle synergies.

Fatigue Influences the Recruitment, but Not Structure, of Muscle Synergies

The development of fatigue elicits multiple adaptations from the neuromuscular system. Muscle synergies are common patterns of neuromuscular activation that have been proposed as the building blocks of human movement. We wanted to identify possible adaptations of muscle synergies to the development of fatigue in the upper limb. Recent studies have reported that synergy structure remains invariant during the development of fatigue, but these studies did not examine isolated synergies. We propose a novel approach to characterise synergy adaptations to fatigue by taking advantage of the spatial tuning of synergies. This approach allows improved identification of changes to individual synergies that might otherwise be confounded by changing contributions of overlapping synergies. To analyse upper limb synergies, we applied non-negative matrix factorization to 14 EMG signals from muscles of 11 participants performing isometric contractions. A preliminary multidirectional task was used to identify synergy directional tuning. A subsequent fatiguing task was designed to fatigue the participants in their synergies’ preferred directions. Both tasks provided virtual reality feedback of the applied force direction and magnitude, and were performed at 40% of each participant’s maximal voluntary force. Five epochs were analysed throughout the fatiguing task to identify progressive changes of EMG amplitude, median frequency, synergy structure, and activation coefficients. Three to four synergies were sufficient to account for the variability contained in the original data. Synergy structure was conserved with fatigue, but interestingly synergy activation coefficients decreased on average by 24.5% with fatigue development. EMG amplitude did not change systematically with fatigue, whereas EMG median frequency consistently decreased across all muscles. These results support the notion of a neuromuscular modular organisation as the building blocks of human movement, with adaptations to synergy recruitment occurring with fatigue. When synergy tuning properties are considered, the reduction of activation of muscle synergies may be a reliable marker to identify fatigue.