Physiological modules for generating discrete and rhythmic movements: component analysis of EMG signals (original) (raw)
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Frontiers in Computational Neuroscience, 2014
A central question in Neuroscience is that of how the nervous system generates the spatiotemporal commands needed to realize complex gestures, such as handwriting. A key postulate is that the central nervous system (CNS) builds up complex movements from a set of simpler motor primitives or control modules. In this study we examined the control modules underlying the generation of muscle activations when performing different types of movement: discrete, point-to-point movements in eight different directions and continuous figure-eight movements in both the normal, upright orientation and rotated 90 • . To test for the effects of biomechanical constraints, movements were performed in the frontal-parallel or sagittal planes, corresponding to two different nominal flexion/abduction postures of the shoulder. In all cases we measured limb kinematics and surface electromyographic activity (EMG) signals for seven different muscles acting around the shoulder. We first performed principal component analysis (PCA) of the EMG signals on a movement-by-movement basis. We found a surprisingly consistent pattern of muscle groupings across movement types and movement planes, although we could detect systematic differences between the PCs derived from movements performed in each shoulder posture and between the principal components associated with the different orientations of the figure. Unexpectedly we found no systematic differences between the figure eights and the point-to-point movements. The first three principal components could be associated with a general co-contraction of all seven muscles plus two patterns of reciprocal activation. From these results, we surmise that both "discreterhythmic movements" such as the figure eight, and discrete point-to-point movement may be constructed from three different fundamental modules, one regulating the impedance of the limb over the time span of the movement and two others operating to generate movement, one aligned with the vertical and the other aligned with the horizontal.
Rhythmic muscular activation pattern for fast figure-eight movement
Clinical Neurophysiology, 2010
Objective: To address the question of how the CNS generates muscle activation patterns for complex gestures, we have chosen to study a figure-eight movement. We hypothesized that the well defined rhythmic aspect of this figure will provide further insights into the temporal features of multi-muscular commands.
Possible contributions of CPG activity to the control of rhythmic human arm movement
Canadian Journal of Physiology and Pharmacology, 2004
There is extensive modulation of cutaneous and H-reflexes during rhythmic leg movement in humans. Mechanisms controlling reflex modulation (e.g., phase-and task-dependent modulation, and reflex reversal) during leg movements have been ascribed to the activity of spinal central pattern generating (CPG) networks and peripheral feedback. Our working hypothesis has been that neural mechanisms (i.e., CPGs) controlling rhythmic movement are conserved between the human lumbar and cervical spinal cord. Thus reflex modulation during rhythmic arm movement should be similar to that for rhythmic leg movement. This hypothesis has been tested by studying the regulation of reflexes in arm muscles during rhythmic arm cycling and treadmill walking. This paper reviews recent studies that have revealed that reflexes in arm muscles show modulation within the movement cycle (e.g., phase-dependency and reflex reversal) and between static and rhythmic motor tasks (e.g., task-dependency). It is concluded that reflexes are modulated similarly during rhythmic movement of the upper and lower limbs, suggesting similar motor control mechanisms. One notable exception to this pattern is a failure of contralateral arm movement to modulate reflex amplitude, which contrasts directly with observations from the leg. Overall, the data support the hypothesis that CPG activity contributes to the neural control of rhythmic arm movement.
Understanding Complex Systems, 2008
Since the seminal paper on phase transitions in bimanual rhythmic movements, research from the dynamical systems perspective has given primacy to rhythmic coordination. While rhythmic movements are a ubiquitous and fundamental expression in biological behavior, non-rhythmic or discrete movements are of similar importance. In fact, rhythmic and discrete movements are commonly intertwined in complex actions. This review traces our strategy of extending a dynamic systems account from rhythmic to non-rhythmic behavior. Behavioral and modeling work on uni-and bimanual, single-and multijoint coordination increasingly investigated more complex movement tasks consisting of rhythmic and discrete elements. The modeling work suggested a three-tiered architecture consisting of a biomechanical, internal and parameter level with different responsibilities for different components of movement generation. A core question raised in the modeling is what are the fundamental units and principles that are tuned to make up complex behavior. Are rhythmic pattern generators the primitives for generating both rhythmic and non-rhythmic behaviors? Alternatively, are discrete pattern generators fundamental, or are there two primitives of action? fMRI experiments compared brain activation in continuously rhythmic and discrete movements. Significantly more activation in discrete movements suggested that discrete movements have higher control demands and may be distinct primitives, different from rhythmic movements. This result corresponds to the modeling work that highlighted that discrete movements require more parameterization. Our behavioral, modeling and imaging research built on and extended the dynamical systems approach to rhythmic coordination with the goal to develop a comprehensive framework to address complex everyday actions in a principled manner.
HUMAN NEUROSCIENCE Control of leg movements driven by EMG activity of shoulder muscles
During human walking, there exists a functional neural coupling between arms and legs, and between cervical and lumbosacral pattern generators. Here, we present a novel approach for associating the electromyographic (EMG) activity from upper limb muscles with leg kinematics. Our methodology takes advantage of the high involvement of shoulder muscles in most locomotor-related movements and of the natural coordination between arms and legs. Nine healthy subjects were asked to walk at different constant and variable speeds (3–5 km/h), while EMG activity of shoulder (deltoid) muscles and the kinematics of walking were recorded. To ensure a high level of EMG activity in deltoid, the subjects performed slightly larger arm swinging than they usually do. The temporal structure of the burst-like EMG activity was used to predict the spatiotemporal kinematic pattern of the forthcoming step. A comparison of actual and predicted stride leg kinematics showed a high degree of correspondence (r > 0.9). This algorithm has been also implemented in pilot experiments for controlling avatar walking in a virtual reality setup and an exoskeleton during over-ground stepping. The proposed approach may have important implications for the design of human–machine interfaces and neuroprosthetic technologies such as those of assistive lower limb exoskeletons.
Scientific reports, 2018
The hand trajectory of motion during the performance of one-dimensional point-to-point movements has been shown to be marked by motor primitives with a bell-shaped velocity profile. Researchers have investigated if motor primitives with the same shape mark also complex upper-limb movements. They have done so by analyzing the magnitude of the hand trajectory velocity vector. This approach has failed to identify motor primitives with a bell-shaped velocity profile as the basic elements underlying the generation of complex upper-limb movements. In this study, we examined upper-limb movements by analyzing instead the movement components defined according to a Cartesian coordinate system with axes oriented in the medio-lateral, antero-posterior, and vertical directions. To our surprise, we found out that a broad set of complex upper-limb movements can be modeled as a combination of motor primitives with a bell-shaped velocity profile defined according to the axes of the above-defined coo...
Order parameters for the neural organization of single, multijoint limb movement patterns
Experimental Brain Research, 1991
Subjects performed two patterns of coordination between the elbow and wrist joints of the right arm: 1) wrist flexion synchronized with elbow flexion and wrist extension with elbow extension (homologous muscle groups); and 2) wrist extension synchronized with elbow flexion and wrist flexion with elbow extension (nonhomologous muscle groups). As a parameter, cycling frequency, was increased, an abrupt switch in the phase relation between the elbow and wrist joints occurred. Similar effects were observed in underlying neuromuscular (EMG) timing patterns. Observed transitions depended on whether the forearm was prone or supine, not simply on the muscle pairing across the joints. With the forearm supine, transitions were from pattern (2) to pattern (1) above, and with the forearm prone the transitions were from pattern (1) to pattern (2). When subjects were initially prepared in pattern (1) with the forearm supine or in pattern (2) with the forearm prone, switching did not occur. En route to transitions, enhanced fluctuations in the phase relation occurred, indicating that loss of stability is at the origin of pattern change. Accompanying such changes in coordination were characteristic effects on end effector trajectories and velocity profiles. Possible neurophysiological mechanisms for context dependence in multijoint coordination are discussed.