scholarly journals When 90% of the variance is not enough: residual EMG from muscle synergy extraction influences task performance

2019 ◽  
Author(s):  
Victor R. Barradas ◽  
Jason J. Kutch ◽  
Toshihiro Kawase ◽  
Yasuharu Koike ◽  
Nicolas Schweighofer
Keyword(s):  
Motor Control ◽  
Dimensionality Reduction ◽  
Muscle Activity ◽  
Muscle Force ◽  
Muscle Synergy ◽  
Muscle Synergies ◽  
Systematic Effect ◽  
Extraction Techniques ◽  
Force Mapping ◽  
Variance Explained

AbstractMuscle synergies are usually identified via dimensionality reduction techniques, such that the identified synergies reconstruct the muscle activity to a level of accuracy defined heuristically, such as 90% of the variance explained. Here, we question the assumption that the residual muscle activity not explained by the synergies is due to noise. We hypothesize instead that the residual activity is structured and can therefore influence the execution of a motor task. Young healthy subjects performed an isometric reaching task in which surface electromyography of 10 arm muscles was mapped onto estimated two-dimensional forces used to control a cursor. Three to five synergies were extracted to account for 90% of the variance explained. We then altered the muscle-force mapping via “hard” and “easy” virtual surgeries. Whereas in both surgeries the forces associated with synergies spanned the same single dimension of the virtual environment, the muscle-force mapping was as close as possible to the initial mapping in the easy surgery and as far as possible in the hard surgery. This design therefore maximized potential differences in reaching errors attributable to the residual muscle activity. Results show that the easy surgery produced much smaller directional errors than the hard task. In addition, systematic estimations of the errors for easy and hard surgeries constructed with 1 to 10 synergies show that the errors differ significantly for up to 8 synergies, which account for 98% of the variance on average. Our study therefore indicates the need for cautious interpretations of results derived from synergy extraction techniques based on heuristics with lenient levels of accuracy.Author summaryThe muscle synergy hypothesis states that the central nervous system simplifies motor control by grouping muscles that share common functions into modules called muscle synergies. Current techniques use unsupervised dimensionality reduction algorithms to identify these synergies. However, these techniques rely on arbitrary criteria to determine the number of synergies, which is actually unknown. An example of such criteria is that the identified synergies must be able to reconstruct the measured muscle activity to at least a 90% level of accuracy. Thus, the residual muscle activity, the remaining 10% of the muscle activity, is often disregarded as noise. We show that residual muscle activity following muscle synergy identification has a large systematic effect on movements even when the number of synergies approaches the number of muscles. This suggests that current synergy extraction techniques may discard a component of muscle activity that is important for motor control. Therefore, current synergy extraction techniques must be updated to identify true physiological synergies.

scholarly journals When 90% of the variance is not enough: residual EMG from muscle synergy extraction influences task performance

2020 ◽  
Vol 123 (6) ◽  
pp. 2180-2190 ◽  
Author(s):  
Victor R. Barradas ◽  
Jason J. Kutch ◽  
Toshihiro Kawase ◽  
Yasuharu Koike ◽  
Nicolas Schweighofer
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Motor Control ◽  
Dimensionality Reduction ◽  
Task Performance ◽  
Muscle Activity ◽  
Muscle Synergy ◽  
Systematic Effect ◽  
Extraction Techniques ◽  
The Central Nervous System

The muscle synergy hypothesis posits that the central nervous system simplifies motor control by grouping muscles into modules. Current techniques use dimensionality reduction, such that the identified synergies reconstruct 90% of the muscle activity. We show that residual muscle activity following such identification can have a large systematic effect on movements, even when the number of synergies approaches the number of muscles. Current synergy extraction techniques must therefore be updated to identify true physiological synergies.

scholarly journals Modular motor control of the sound limb in gait of people with trans-femoral amputation

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Cristiano De Marchis ◽  
Simone Ranaldi ◽  
Mariano Serrao ◽  
Alberto Ranavolo ◽  
Francesco Draicchio ◽  
...  
Keyword(s):  
Motor Control ◽  
Lower Limb ◽  
Muscle Activity ◽  
Healthy Subjects ◽  
Spatial Organization ◽  
Adaptation Strategy ◽  
Muscle Synergy ◽  
Heel Strike ◽  
Muscle Synergies ◽  
Control Schemes

Abstract Background The above-knee amputation of a lower limb is a severe impairment that affects significantly the ability to walk; considering this, a complex adaptation strategy at the neuromuscular level is needed in order to be able to move safely with a prosthetic knee. In literature, it has been demonstrated that muscle activity during walking can be described via the activation of a small set of muscle synergies. The analysis of the composition and the time activation profiles of such synergies have been found to be a valid tool for the description of the motor control schemes in pathological subjects. Methods In this study, we used muscle synergy analysis techniques to characterize the differences in the modular motor control schemes between a population of 14 people with trans-femoral amputation and 12 healthy subjects walking at two different (slow and normal self-selected) speeds. Muscle synergies were extracted from a 12 lower-limb muscles sEMG recording via non-negative matrix factorization. Equivalence of the synergy vectors was quantified by a cross-validation procedure, while differences in terms of time activation coefficients were evaluated through the analysis of the activity in the different gait sub-phases. Results Four synergies were able to reconstruct the muscle activity in all subjects. The spatial component of the synergy vectors did not change in all the analysed populations, while differences were present in the activity during the sound limb’s stance phase. Main features of people with trans-femoral amputation’s muscle synergy recruitment are a prolonged activation of the module composed of calf muscles and an additional activity of the hamstrings’ module before and after the prosthetic heel strike. Conclusions Synergy-based results highlight how, although the complexity and the spatial organization of motor control schemes are the same found in healthy subjects, substantial differences are present in the synergies’ recruitment of people with trans femoral amputation. In particular, the most critical task during the gait cycle is the weight transfer from the sound limb to the prosthetic one. Future studies will integrate these results with the dynamics of movement, aiming to a complete neuro-mechanical characterization of people with trans-femoral amputation’s walking strategies that can be used to improve the rehabilitation therapies.

scholarly journals Motor Control System for Adaptation of Healthy Individuals and Recovery of Poststroke Patients: A Case Study on Muscle Synergies

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Fady S. Alnajjar ◽  
Juan C. Moreno ◽  
Ken-ichi Ozaki ◽  
Izumi Kondo ◽  
Shingo Shimoda
Keyword(s):  
Motor Control ◽  
Control System ◽  
Motor Recovery ◽  
Muscle Synergy ◽  
Behavioral Adaptation ◽  
Muscle Synergies ◽  
Healthy Individuals ◽  
Familiar Environment ◽  
Motor Control System ◽  
Healthy Participants

Understanding the complex neuromuscular strategies underlying behavioral adaptation in healthy individuals and motor recovery after brain damage is essential for gaining fundamental knowledge on the motor control system. Relying on the concept of muscle synergy, which indicates the number of coordinated muscles needed to accomplish specific movements, we investigated behavioral adaptation in nine healthy participants who were introduced to a familiar environment and unfamiliar environment. We then compared the resulting computed muscle synergies with those observed in 10 moderate-stroke survivors throughout an 11-week motor recovery period. Our results revealed that computed muscle synergy characteristics changed after healthy participants were introduced to the unfamiliar environment, compared with those initially observed in the familiar environment, and exhibited an increased neural response to unpredictable inputs. The altered neural activities dramatically adjusted through behavior training to suit the unfamiliar environment requirements. Interestingly, we observed similar neuromuscular behaviors in patients with moderate stroke during the follow-up period of their motor recovery. This similarity suggests that the underlying neuromuscular strategies for adapting to an unfamiliar environment are comparable to those used for the recovery of motor function after stroke. Both mechanisms can be considered as a recall of neural pathways derived from preexisting muscle synergies, already encoded by the brain’s internal model. Our results provide further insight on the fundamental principles of motor control and thus can guide the future development of poststroke therapies.

scholarly journals A network information theoretic framework to characterise muscle synergies in space and time

2021 ◽  
Author(s):  
David O'Reilly ◽  
Ioannis Delis
Keyword(s):  
Dimensionality Reduction ◽  
Motor System ◽  
Large Scale ◽  
Muscle Synergy ◽  
Whole Body ◽  
Muscle Synergies ◽  
Spatial And Temporal Scales ◽  
Network Information ◽  
Benchmark Datasets ◽  
Muscle Activations

Coordinated movement is thought to be simplified by the nervous system through the activation of muscle synergies. Current approaches to muscle synergy extraction rely on dimensionality reduction algorithms that impose limiting constraints. To capture large-scale interactions between muscle activations, a more generalised approach that considers the complexity and nonlinearity of the motor system is required. Here we developed a novel framework for muscle synergy extraction that relaxes model assumptions by using a combination of information- and network theory and dimensionality reduction. This novel framework can capture spatial, temporal and spatiotemporal interactions, producing distinct spatial groupings and both tonic and phasic temporal patterns. Furthermore, our framework identifies submodular structures in the extracted synergies that exemplify the fractal modularity of the human motor system. To demonstrate the versatility of the methodology, we applied it to two benchmark datasets of arm and whole-body reaching movements. Readily interpretable muscle synergies spanning multiple spatial and temporal scales were identified that demonstrated significant task dependence, ability to capture trial-to-trial fluctuations, a scale-invariance with dataset complexity and a substantial concordance across participants. Finally, we position this framework as a bridge between existing models and recent network-theoretic endeavours by discussing the continuity and novelty of the presented findings.

scholarly journals Can Measured Synergy Excitations Accurately Construct Unmeasured Muscle Excitations?

2017 ◽  
Vol 140 (1) ◽  
Author(s):  
Nicholas A. Bianco ◽  
Carolynn Patten ◽  
Benjamin J. Fregly
Keyword(s):  
Muscle Force ◽  
Human Movement ◽  
Optimization Methods ◽  
Contact Forces ◽  
Muscle Synergy ◽  
Muscle Synergies ◽  
Large Set ◽  
Dimensional Representation ◽  
Joint Contact ◽  
Low Dimensional

Accurate prediction of muscle and joint contact forces during human movement could improve treatment planning for disorders such as osteoarthritis, stroke, Parkinson's disease, and cerebral palsy. Recent studies suggest that muscle synergies, a low-dimensional representation of a large set of muscle electromyographic (EMG) signals (henceforth called “muscle excitations”), may reduce the redundancy of muscle excitation solutions predicted by optimization methods. This study explores the feasibility of using muscle synergy information extracted from eight muscle EMG signals (henceforth called “included” muscle excitations) to accurately construct muscle excitations from up to 16 additional EMG signals (henceforth called “excluded” muscle excitations). Using treadmill walking data collected at multiple speeds from two subjects (one healthy, one poststroke), we performed muscle synergy analysis on all possible subsets of eight included muscle excitations and evaluated how well the calculated time-varying synergy excitations could construct the remaining excluded muscle excitations (henceforth called “synergy extrapolation”). We found that some, but not all, eight-muscle subsets yielded synergy excitations that achieved >90% extrapolation variance accounted for (VAF). Using the top 10% of subsets, we developed muscle selection heuristics to identify included muscle combinations whose synergy excitations achieved high extrapolation accuracy. For 3, 4, and 5 synergies, these heuristics yielded extrapolation VAF values approximately 5% lower than corresponding reconstruction VAF values for each associated eight-muscle subset. These results suggest that synergy excitations obtained from experimentally measured muscle excitations can accurately construct unmeasured muscle excitations, which could help limit muscle excitations predicted by muscle force optimizations.

scholarly journals Cortical control of locomotor muscle activity through muscle synergies in humans: a neural decoding study

2018 ◽  
Author(s):  
Hikaru Yokoyama ◽  
Naotsugu Kaneko ◽  
Tetsuya Ogawa ◽  
Noritaka Kawashima ◽  
Katsumi Watanabe ◽  
...  
Keyword(s):  
Muscle Activity ◽  
Muscle Activation ◽  
Indirect Evidence ◽  
Muscle Synergy ◽  
Muscle Synergies ◽  
Neural Decoding ◽  
Individual Muscle ◽  
Cortical Involvement ◽  
Decoding Accuracy

AbstractWalking movements are orchestrated by the activation of a large number of muscles. The control of numerous muscles during walking is believed to be simplified by flexible activation of groups of muscles called muscle synergies. Although significant corticomuscular connectivity during walking has been reported, the level at which the cortex controls locomotor muscle activity (i.e., muscle synergy or individual muscle level) remains unclear. Here, we examined cortical involvement in muscle control during walking by brain decoding of the activation of muscle synergies and individual muscles from electroencephalographic (EEG) signals using linear decoder models. First, we demonstrated that activation of locomotor muscle synergies was decoded from slow cortical waves with significant accuracy. In addition, we found that decoding accuracy for muscle synergy activation was greater than that for individual muscle activation and that decoding of individual muscle activation was based on muscle synergy-related cortical information. Taken together, these results provide indirect evidence that the cerebral cortex hierarchically controls multiple muscles through a few muscle synergies during walking. Our findings extend the current understanding of the role of the cortex in muscular control during walking and could accelerate the development of effective brain-machine interfaces for people with locomotor disabilities.

scholarly journals Absence of postural muscle synergies for balance after spinal cord transection

2013 ◽  
Vol 110 (6) ◽  
pp. 1301-1310 ◽  
Author(s):  
Stacie A. Chvatal ◽  
Jane M. Macpherson ◽  
Gelsy Torres-Oviedo ◽  
Lena H. Ting
Keyword(s):  
Spinal Cord ◽  
Muscle Activity ◽  
Balance Control ◽  
Neural Mechanisms ◽  
Spinal Cord Transection ◽  
Muscle Synergy ◽  
Modular Organization ◽  
Muscle Synergies ◽  
Weight Support ◽  
Postural Muscle

Although cats that have been spinalized can also be trained to stand and step with full weight support, directionally appropriate long-latency responses to perturbations are impaired, suggesting that these behaviors are mediated by distinct neural mechanisms. However, it remains unclear whether these responses reflect an attenuated postural response using the appropriate muscular coordination patterns for balance or are due to fundamentally different neural mechanisms such as increased muscular cocontraction or short-latency stretch responses. Here we used muscle synergy analysis on previously collected data to identify whether there are changes in the spatial organization of muscle activity for balance within an animal after spinalization. We hypothesized that the modular organization of muscle activity for balance control is disrupted by spinal cord transection. In each of four animals, muscle synergies were extracted from postural muscle activity both before and after spinalization with nonnegative matrix factorization. Muscle synergy number was reduced after spinalization in three animals and increased in one animal. However, muscle synergy structure was greatly altered after spinalization with reduced direction tuning, suggesting little consistent organization of muscle activity. Furthermore, muscle synergy recruitment was correlated to subsequent force production in the intact but not spinalized condition. Our results demonstrate that the modular structure of sensorimotor feedback responses for balance control is severely disrupted after spinalization, suggesting that the muscle synergies for balance control are not accessible by spinal circuits alone. Moreover, we demonstrate that spinal mechanisms underlying weight support are distinct from brain stem mechanisms underlying directional balance control.

scholarly journals Children With Cerebral Palsy Have Greater Stride-to-Stride Variability of Muscle Synergies During Gait Than Typically Developing Children: Implications for Motor Control Complexity

2018 ◽  
Vol 32 (9) ◽  
pp. 834-844 ◽  
Author(s):  
Yushin Kim ◽  
Thomas C. Bulea ◽  
Diane L. Damiano
Keyword(s):  
Cerebral Palsy ◽  
Motor Control ◽  
Clinical Utility ◽  
Neuromuscular Control ◽  
Muscle Synergy ◽  
Neurological Deficits ◽  
Muscle Synergies ◽  
Children With Cerebral Palsy ◽  
The Central Nervous System ◽  
Potential Clinical Utility

Background. There is mounting evidence that the central nervous system utilizes a modular approach for neuromuscular control of walking by activating groups of muscles in units termed muscle synergies. Examination of muscle synergies in clinical populations may provide insights into alteration of neuromuscular control underlying pathological gait patterns. Previous studies utilizing synergy analysis have reported reduced motor control complexity during walking in those with neurological deficits, revealing the potential clinical utility of this approach. Methods. We extracted muscle synergies on a stride-to-stride basis from 20 children with cerebral palsy (CP; Gross Motor Function Classification System levels I-II) and 8 children without CP, allowing the number of synergies to vary for each stride. Similar muscle synergies across all participants and strides were grouped using a k-means clustering and discriminant analysis. Results. In total, 10 clusters representing 10 distinct synergies were found across the 28 individuals. Relative to their total number of synergies deployed during walking, synergies from children with CP were present in a higher number of clusters than in children with typical development (TD), indicating significantly greater stride-to-stride variability. This increased variability was present despite reduced complexity, as measured by the mean number of synergies in each stride. Whereas children with CP demonstrate some novel synergies, they also deploy some of the same muscle synergies as those with TD, although less frequently and with more variability. Conclusion. A stride-by-stride approach to muscle synergy analysis expands its clinical utility and may provide a method to tailor rehabilitation strategies by revealing inconsistent but functional synergies in each child with CP.

scholarly journals Do Walking Muscle Synergies Influence Propensity of Severe Slipping?

2019 ◽  
Author(s):  
Mohammad Moein Nazifi ◽  
Kurt Beschorner ◽  
Pilwon Hur
Keyword(s):  
Young Adults ◽  
Motor Control ◽  
High Risk ◽  
Fall Prevention ◽  
Diagnostic Techniques ◽  
Muscle Synergy ◽  
Muscle Synergies ◽  
Control Patterns ◽  
Risk Of Fall

AbstractSlipping is frequently responsible for falling injuries. Preventing slips, and more importantly severe slips, is of importance in fall prevention. Our previous study characterized mild slipping and severe slipping by the analysis of muscle synergies. Significant discrepancies in motor control of slipping have been observed between mild and severe slippers. We are further interested in whether differences exist in baseline motor control patterns between persons who experience mild and severe slips when exposed to a slippery contaminant. This study investigated walking with a muscle synergy approach to detect if walking muscle synergies differ between groups experiencing different slip severities. Twenty healthy young adults (8 mild slippers and 12 severe slippers) participated in this study and their muscle synergies of walking were extracted. Muscle synergy analysis showed that mild slippers had a higher contribution of hamstring and quadriceps during walking while severe slippers had increased contribution of tibialis group. This study provides novel information that may contribute to identifying diagnostic techniques for identifying persons or populations with a high risk of fall based on their walking patterns.

Sign in / Sign up

Export Citation Format

Share Document