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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 "discrete-rhythmic 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.
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http://dx.doi.org/10.3389/fncom.2014.00169 | DOI Listing |
ACS Sens
September 2025
State Key Laboratory of Advanced Fiber Materials, College of Materials Science and Engineering, Donghua University, Shanghai 201620, China.
High-fidelity biosignal monitoring is essential for daily health tracking and the diagnosis of chronic diseases. However, developing bioelectrodes capable of withstanding repeated use and mechanical deformation on wet tissue surfaces remains a significant challenge. Here, we present a robust and ultrathin bioelectrode (RUB), featuring a mechanically heterogeneous architecture and a thickness of ∼3 μm.
View Article and Find Full Text PDFBiosens Bioelectron
September 2025
UCD Centre for Biomedical Engineering, University College Dublin, Belfield, Dublin, 4, Ireland; School of Mechanical & Materials Engineering, University College Dublin, Belfield, Dublin, 4, Ireland. Electronic address:
Surface electromyography (sEMG) is the measurement of the electrical activity of muscle and is extensively used in fundamental research and across many applications in health and sport. Conventional surface electrode technology can suffer from poor signal quality, particularly when used outside the laboratory, requires careful skin preparation prior to electrode application, and can be challenging when used for long-term recording. These limitations have challenged the translation of sEMG to widespread clinical application.
View Article and Find Full Text PDFIEEE Trans Neural Syst Rehabil Eng
September 2025
Recognizing hand gestures from surface electromyography (sEMG) signals is crucial for neural interfaces and human-machine interaction. However, developing subject-generic models remains challenging due to substantial inter-subject variability. Complicating matters further, the muscle groups driving gestures with varying degrees of freedom (DoFs) often overlap, producing highly convoluted feature distributions across subjects and DoFs.
View Article and Find Full Text PDFPsychophysiology
September 2025
Department of Psychology, Clinical Psychology and Psychotherapy, University of Regensburg, Regensburg, Germany.
Facial emotional expressions are interactive signals that communicate intentions. Previous research has shown that sending a facial emotional expression influences the evaluation of response expressions, but the mechanisms behind this effect remain unclear. In a preregistered experiment, 68 participants were asked to send an emoji (happy, neutral, and angry) to a virtual agent in front of them, whereupon the agent reacted with either a smiling or frowning facial expression.
View Article and Find Full Text PDFPflugers Arch
September 2025
Department of Science, University "G. d'Annunzio" Chieti-Pescara, Chieti, Italy.
Hypoxia has been extensively studied as a stressor which pushes human bodily systems to responses and adaptations. Nevertheless, a few evidence exist onto constituent trains of motor unit action potential, despite recent advancements which allow to decompose surface electromyographic signals. This study aimed to investigate motor unit properties from noninvasive approaches during maximal isometric exercise in normobaric hypoxia.
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