Motor Control Techniques: Integrating Motor Neuroscience and Applied Mathematics
A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".
Deadline for manuscript submissions: 31 January 2026 | Viewed by 2
Special Issue Editor
Interests: applied mathematics; neuroscience; motor control; heart-brain and brain-muscle coherence; transcranial direct current stimulation (tDCS neuromodulation)
Special Issue Information
Dear Colleagues,
The intricate mechanisms of motor control, from the neural encoding of intention to biomechanical execution, represent some of the most complex systems in biology. Historically, motor neuroscience and applied mathematics have progressed in parallel; however, advances in neural recording (e.g., EEG, EMG, and neuroimaging) and powerful computational tools now enable unprecedented synergy.
This Special Issue, “Motor Control Techniques: Integrating Motor Neuroscience and Applied Mathematics,” will highlight cutting-edge research at this promising intersection of disciplines. We invite contributions that leverage quantitative and theoretical approaches, such as computational modeling, advanced signal processing, machine learning, and non-invasive neuromodulation, to unravel the fundamental principles of motor control in health and disease. Emphasis will be placed on studies that provide novel insights into neural processes and offer tangible strategies for clinical translation, including utilizing robust biomarkers for neurodegenerative disorders, optimized rehabilitation approaches, and the development of next-generation neuroprosthetics.
By bridging theoretical constructs and empirical data, this Special Issue will highlight the state of the art and guide the future of integrated motor control science. We invite scholars to contribute original research that advances the boundaries of this rapidly evolving field.
Dr. Osmar Pinto Neto
Guest Editor
Manuscript Submission Information
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Keywords
- motor control
- computational neuroscience
- neural oscillations
- electromyography (EMG)
- neuromodulation (tDCS, TMS)
- Bayesian models
- motor learning and adaptation
- biomechanics
- rehabilitation engineering
- Parkinson's disease
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