Roles of the elements of the triphasic control signal

Hannaford, Blake; Stark, Lawrence

doi:10.1016/0014-4886(85)90160-8

Cited by 291 publications

(60 citation statements)

References 19 publications

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“…Wrist flexors exhibited bursts shortly before movement onset. These burst sequences in the forearm muscles are similar to the triphasic EMG pattern typical for ramp and hold movements, where a burst of activity in the agonist muscles (wrist extensors in our case) is followed by a pause in EMG activity in those muscles (Brown and Cooke 1990;Hannaford and Stark 1985;Marsden et al 1983). The antagonist muscles (wrist flexors) then burst, and finally antagonists relax as the agonists reactivate.…”

Section: Resultssupporting

confidence: 72%

Joint cross-correlation analysis reveals complex, time-dependent functional relationship between cortical neurons and arm electromyograms

Zhuang

Lebedev

Nicolelis

2014

Journal of Neurophysiology

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Zhuang KZ, Lebedev MA, Nicolelis MA. Joint cross-correlation analysis reveals complex, time-dependent functional relationship between cortical neurons and arm electromyograms. J Neurophysiol 112: 2865-2887, 2014. First published September 10, 2014 doi:10.1152/jn.00031.2013.-Correlation between cortical activity and electromyographic (EMG) activity of limb muscles has long been a subject of neurophysiological studies, especially in terms of corticospinal connectivity. Interest in this issue has recently increased due to the development of brain-machine interfaces with output signals that mimic muscle force. For this study, three monkeys were implanted with multielectrode arrays in multiple cortical areas. One monkey performed self-timed touch pad presses, whereas the other two executed arm reaching movements. We analyzed the dynamic relationship between cortical neuronal activity and arm EMGs using a joint cross-correlation (JCC) analysis that evaluated trial-by-trial correlation as a function of time intervals within a trial. JCCs revealed transient correlations between the EMGs of multiple muscles and neural activity in motor, premotor and somatosensory cortical areas. Matching results were obtained using spike-triggered averages corrected by subtracting trial-shuffled data. Compared with spike-triggered averages, JCCs more readily revealed dynamic changes in cortico-EMG correlations. JCCs showed that correlation peaks often sharpened around movement times and broadened during delay intervals. Furthermore, JCC patterns were directionally selective for the arm-reaching task. We propose that such highly dynamic, taskdependent and distributed relationships between cortical activity and EMGs should be taken into consideration for future brain-machine interfaces that generate EMG-like signals.

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Section: Resultssupporting

confidence: 72%

Joint cross-correlation analysis reveals complex, time-dependent functional relationship between cortical neurons and arm electromyograms

Zhuang

Lebedev

Nicolelis

2014

Journal of Neurophysiology

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“…Very few results have been presented on attempts at control of the antagonistically coupled compliant drives [28,37]. In this section we focus on energy efficiency and suggest a new control approach inspired by patterns of electromyography signals measured in antagonistically coupled human muscles [21].…”

Section: Puller-follower Controlmentioning

confidence: 99%

Toward Anthropomimetic Robotics: Development, Simulation, and Control of a Musculoskeletal Torso

et al. 2013

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A version of this paper with color figures is available online at http://dx.doi.org/10.1162/ artl_a_00088. Subscription required.Abstract Anthropomimetic robotics differs from conventional approaches by capitalizing on the replication of the inner structures of the human body, such as muscles, tendons, bones, and joints. Here we present our results of more than three years of research in constructing, simulating, and, most importantly, controlling anthropomimetic robots. We manufactured four physical torsos, each more complex than its predecessor, and developed the tools required to simulate their behavior. Furthermore, six different control approaches, inspired by classical control theory, machine learning, and neuroscience, were developed and evaluated via these simulations or in small-scale setups. While the obtained results are encouraging, we are aware that we have barely exploited the potential of the anthropomimetic design so far. But, with the tools developed, we are confident that this novel approach will contribute to our understanding of morphological computation and human motor control in the future.

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“…Extensive experimental studies have been carried out to identify biological control strategies of movements. One class of models based on experimental observations focuses on the triphasic muscle activation pattern which is evident for both single-joint and multi-joint movements (Lestienne 1979;Ghez and Martin 1982;Marsden et al 1983;Hannaford and Stark 1985;Karst and Hasan, 1991a,b;Flanders et al 1994). In the impulse-timing model, Wallace (1981) assumed that a motor program could calculate the timing and duration of excitation pulses for the agonist and antagonist muscles.…”

Section: Introductionmentioning

confidence: 99%

Analysis of an optimal control model of multi-joint arm movements

Lan

1997

Biological Cybernetics

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Abstract. In this paper, we propose a model of biological motor control for generation of goal-directed multi-joint arm movements, and study the formation of muscle control inputs and invariant kinematic features of movements. The model has a hierarchical structure that can determine the control inputs for a set of redundant muscles without any inverse computation. Calculation of motor commands is divided into two stages, each of which performs a transformation of motor commands from one coordinate system to another. At the first level, a central controller in the brain accepts instructions from higher centers, which represent the motor goal in the Cartesian space. The controller computes joint equilibrium trajectories and excitation signals according to a minimum effort criterion. At the second level, a neural network in the spinal cord translates the excitation signals and equilibrium trajectories into control commands to three pairs of antagonist muscles which are redundant for a two-joint arm. No inverse computation is required in the determination of individual muscle commands. The minimum effort controller can produce arm movements whose dynamic and kinematic features are similar to those of voluntary arm movements. For fast movements, the hand approaches a target position along a near-straight path with a smooth bell-shaped velocity. The equilibrium trajectories in X and ½ show an 'N' shape, but the end-point equilibrium path zigzags around the hand path. Joint movements are not always smooth. Joint reversal is found in movements in some directions. The excitation signals have a triphasic (or biphasic) pulse pattern, which leads to stereotyped triphasic (or biphasic) bursts in muscle control inputs, and a dynamically modulated joint stiffness. There is a fixed sequence of muscle activation from proximal muscles to distal muscles. The order is preserved in all movements. For slow movements, it is shown that a constant joint stiffness is necessary to produce a smooth movement with a bell-shaped velocity. Scaled movements can be reproduced by varying the constraints on the maximal level of excitation signals according to the speed of movement.When the inertial parameters of the arm are altered, movement trajectories can be kept invariant by adjusting the pulse height values, showing the ability to adapt to load changes. These results agree with a wide range of experimental observations on human voluntary movements.

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Roles of the elements of the triphasic control signal

Cited by 291 publications

References 19 publications

Joint cross-correlation analysis reveals complex, time-dependent functional relationship between cortical neurons and arm electromyograms

Joint cross-correlation analysis reveals complex, time-dependent functional relationship between cortical neurons and arm electromyograms

Toward Anthropomimetic Robotics: Development, Simulation, and Control of a Musculoskeletal Torso

Analysis of an optimal control model of multi-joint arm movements

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