Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

Pollard

Gritsenko

2017

Front. Hum. Neurosci.

Self Cite

Human reaching movements require complex muscle activations to produce the forces necessary to move the limb in a controlled manner. How gravity and the complex kinetic properties of the limb contribute to the generation of the muscle activation pattern by the central nervous system (CNS) is a long-standing and controversial question in neuroscience. To tackle this issue, muscle activity is often subdivided into static and phasic components. The former corresponds to posture maintenance and transitions between postures. The latter corresponds to active movement production and the compensation for the kinetic properties of the limb. In the present study, we improved the methodology for this subdivision of muscle activity into static and phasic components by relating them to joint torques. Ten healthy subjects pointed in virtual reality to visual targets arranged to create a standard center-out reaching task in three dimensions. Muscle activity and motion capture data were synchronously collected during the movements. The motion capture data were used to calculate postural and dynamic components of active muscle torques using a dynamic model of the arm with 5 degrees of freedom. Principal Component Analysis (PCA) was then applied to muscle activity and the torque components, separately, to reduce the dimensionality of the data. Muscle activity was also reconstructed from gravitational and dynamic torque components. Results show that the postural and dynamic components of muscle torque represent a significant amount of variance in muscle activity. This method could be used to define static and phasic components of muscle activity using muscle torques.

Section: Methodsmentioning

confidence: 99%

Gravitational and Dynamic Components of Muscle Torque Underlie Tonic and Phasic Muscle Activity during Goal-Directed Reaching

Pollard

Gritsenko

2017

Front. Hum. Neurosci.

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“…Muscles recorded during the experiment included the pectoralis major (PM), anterior deltoid (AD), posterior deltoid (PD), long and lateral heads of the triceps (TLo and TLa respectively), short and long heads of the biceps (BS and BL respectively), brachioradialis (BR), flexor carpi radialis (FCR), extensor carpi radialis (ECR), teres major (TM), and flexor carpi ulnaris (FCU); also numbered 1–12 respectively in figures. The recorded signals were synchronized through a common trigger generated by VR software and a custom circuit [8]. All data analysis of recorded signals was done in MATLAB (Mathworks, Inc).…”

Section: Methodsmentioning

confidence: 99%

A novel method of identifying motor primitives using wavelet decomposition

Попов

2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

Yakovenko

et al. 2018

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This study reports a new technique for extracting muscle synergies using continuous wavelet transform. The method allows to quantify coincident activation of muscle groups caused by the physiological processes of fixed duration, thus enabling the extraction of wavelet modules of arbitrary groups of muscles. Hierarchical clustering and identification of the repeating wavelet modules across subjects and across movements, was used to identify consistent muscle synergies. Results indicate that the most frequently repeated wavelet modules comprised combinations of two muscles that are not traditional agonists and span different joints. We have also found that these wavelet modules were flexibly combined across different movement directions in a pattern resembling directional tuning. This method is extendable to multiple frequency domains and signal modalities.

“…Muscles recorded during the experiment included the pectoralis major (Pec), teres major (TrM), anterior deltoid (AD), posterior deltoid (PD), long and short heads of the biceps (BiL and BiS respectively), lateral and long heads of the triceps (TrLa and TrLo respectively), brachioradialis (Br), flexor carpi ulnaris (FCU), flexor carpi radialis (FCR), and extensor carpi radialis (ECR). Motion capture and electromyography were synchronized using a custom circuit and triggering mechanism (Talkington et al, 2015). Motion capture and EMG data were imported into Matlab and processed as follows using custom scripts.…”

Section: Methodsmentioning

confidence: 99%

Gravitational and dynamic components of muscle torque underlie tonic and phasic muscle activity during goal-directed reaching

Pollard

Gritsenko

2017

Preprint

Self Cite

Human reaching movements require complex muscle activations to produce the forces necessary to move the limb in a controlled manner. How gravity and the complex kinetic properties of the limb contribute to the generation of the muscle activation pattern by the central nervous system (CNS) is a longstanding question in neuroscience. To address this question, muscle activity is often subdivided into static and phasic components. The former is thought to be related to posture maintenance and transitions between postures. The latter represents the remainder of muscle activity and is thought to be related to active movement production and the compensation for the kinetic properties of the limb. In the present study, we directly addressed how this subdivision of muscle activity into static and phasic components is related to the corresponding components of active muscle torques. Eight healthy subjects pointed in virtual reality to visual targets arranged to create a standard center-out reaching task in three dimensions. Muscle activity and motion capture data were synchronously collected during the movements. The motion capture data were used to calculate gravitational and dynamic components of active muscle torques using a dynamic model of the arm with 5 degrees of freedom. Principal Component Analysis (PCA) was then applied to muscle activity and the torque components, separately, to reduce the dimensionality of the data. Muscle activity was also reconstructed from gravitational and dynamic torque components. Results show that the gravitational and dynamic components of muscle torque represent a significant amount of variance in muscle activity. This method could be used to identify static and phasic components of muscle activity using muscle torques. The contribution of both components to the overall muscle activity was largely equal, unlike their relative contribution to active muscle torques, which may reflect a neural control strategy.