Antonio Gogeascoechea

Understanding how the central nervous system controls complex movements, such as walking, remains a fundamental challenge. Although motor units (MUs) are well-studied in isometric tasks, their role in generating joint moments during functional dynamic movements is unclear, partly due to non-stationary conditions. Filling this knowledge gap is essential for studying neural control of walking at the cellular level and for guiding neurorehabilitation and robotic interventions. We developed a validated framework for decoding high-density electromyography (HD-EMG) into individual MU spike trains during walking. We assessed both static decoding and an adaptive algorithm that continuously tracks time-varying action potentials, validating the results against fine-wire intra-muscular EMG (iEMG). We then examined neuromechanical delays (NMD) across walking speeds and established MU-driven neuromusculoskeletal models to determine the biomechanical consequences of the decoded MU firing patterns. Five healthy adults walked at multiple speeds while we recorded HD-EMG, iEMG, motion capture, and ground reaction forces. Both static and adaptive decompositions yielded comparable MU spike trains. Median rate of agreement was higher and false positives were lower for the adaptive approach, whereas false negatives were lower for the static approach. Although not statistically significant (p>0.05), these trends suggest the adaptive method may better handle nonstationarities. NMD decreased with speed, indicating coherent acceleration of neural-to-mechanical transmission. MU-driven models reproduced ankle joint moments with substantially lower normalized RMSE than conventional EMG-envelope models (p<0.05), with no difference between static and adaptive models. The close match between MU-derived and measured joint moments confirms that the decoded neural drive captures functional motor control rather than numerical artifacts. This study validates MU decomposition during walking and establishes a MU-driven model-based framework for investigating spinal motor control under dynamic, functionally relevant conditions. By bridging cellular-level neural activity with biomechanical outcomes, our approach opens new avenues for advancing neuro-rehabilitation, assistive technology, and our understanding of human locomotion.

The development of human-machine interfaces is key to creating prosthetics that adapt to user intent. However, there remains much incongruity between muscle activity and the resulting torque. This study employs high-density surface electromyography (HD-sEMG) in conjunction with the Fast-ICA algorithm to decompose motor units (MUs), which play a crucial role in the generation of movement. Multilayer perceptron (MLP) architectures are evaluated for predicting joint torque from MU activity, and transfer learning is explored to enhance training performance. Results show that transfer learning enhances training efficiency and that joint torque can be accurately predicted from MU activity, thereby advancing the potential of control systems in assistive technologies.

The central nervous system employs distinct motor control strategies depending on task demands. Accordingly, the activity of alpha-motoneuron (MN) pools innervating skeletal muscle fibers is modulated based on muscle force and rate of force development (RFD). In human subjects, biophysical MN models enable inferring in vivo the neural processes ( e.g. , synaptic input, activity of the entire MN pool, etc.) underlying this modulation, which are otherwise challenging to measure experimentally. Due to unique neurophysiological characteristics of individuals, personalizing these models is essential to study motor control in humans. Therefore, this work studied the mechanisms involved in the modulation of RFD using person-specific MN pool models driven by in vivo common synaptic input estimates ( i.e. , derived from surface high-density electromyography). Specifically, we assessed how in vivo MN activity changed across RFD and muscle force. This included modulation of recruitment and rate coding in the complete MN pool, as well as model-based estimates of excitatory synaptic gains (ΔIF). We found RFD-specific changes in MN activity associated to changes in ΔIF. Moreover, we showed that MN pool models driven by RFD-specific ΔIFs reproduced in vivo MN firing features and associated force profiles at different RFDs. Altogether, this work represents a step towards modelling the mechanisms of force generation in humans and creating person-specific models of the spinal circuitry. This will open a window for studying in vivo human neuromechanics and motor restoring interventions.

Understanding how motor units (MUs) contribute to skeletal mechanical force is crucial for unraveling the underlying mechanism of human movement. Alterations in MU firing, contractile and force-generating properties emerge in response to physical training, ageing or injury. However, how changes in MU firing and twitch properties dictate skeletal muscle force generation in healthy and impaired individuals remains an open question. In this work, we present a MU-specific approach to identify firing and twitch properties of MU samples and employ them to decode musculoskeletal function in vivo. First, MU firing events were decomposed offline from high-density electromyography (EMG) of six lower leg muscles involved in ankle plantar-dorsi flexion.We characterized their twitch responses based on the statistical distributions of their firing properties and employed them to compute MU-specific activation dynamics. Subsequently, we decoded ankle joint moments by linking our framework to a subject-specific musculoskeletal model. We validated our approach at different ankle positions and levels of activation and compared it with traditional EMG-driven models. Our proposed MU-specific formulation achieves higher generalization across conditions than the EMG-driven models, with significantly lower coefficients of variation in torque predictions. Furthermore, our approach shows distinct neural strategies across a large repertoire of contractile conditions in different muscles. Our proposed approach may open new avenues for characterizing the relationship between MU firing and twitch properties and their influence on force capacity. This can facilitate the development of targeted rehabilitation strategies tailored to individuals with specific neuromuscular conditions.

Interfacing with alpha-motoneurons (MNs) is key to understand and control motor impairment and neurorehabilitation technologies. Depending on the neurophysiological condition of each individual, MN pools exhibit distinct neuro-anatomical properties and firing behaviors. Hence, the ability to assess subject-specific characteristics of MN pools is essential for unravelling the neural mechanisms and adaptations underlying motor control, both in healthy and impaired individuals. However, measuring in vivo the properties of complete human MN pools remains an open challenge. Therefore, this work proposes a novel approach based on decoding neural discharges from human MNs in vivo for driving the metaheuristic optimization of biophysically realistic MN models. First, we show that this framework provides subject-specific estimates of MN pool properties from the tibialis anterior muscle on five healthy individuals. Second, we propose a methodology to create complete pools of in silico MNs for each subject. Lastly, we show that neural-data driven complete in silico MN pools reproduce in vivo MN firing characteristics and muscle activation profiles during force-tracking tasks involving isometric ankle dorsi-flexion, at different levels of amplitude. This approach can open new avenues for understanding human neuro-mechanics and, particularly, MN pool dynamics, in a person-specific way. Thereby enabling the development of personalized neurorehabilitation and motor restoring technologies.