Quantification of high and low sEMG spectral components during sustained isometric contraction

Dynamic causal model application on hierarchical human motor control estimation in visuomotor tasks

Introduction

In brain function research, each brain region has been investigated independently, and how different parts of the brain work together has been examined using the correlations among them. However, the dynamics of how different brain regions interact with each other during time-varying tasks, such as voluntary motion tasks, are still not well-understood.

Methods

To address this knowledge gap, we conducted functional magnetic resonance imaging (fMRI) using target tracking tasks with and without feedback. We identified the motor cortex, cerebellum, and visual cortex by using a general linear model during the tracking tasks. We then employed a dynamic causal model (DCM) and parametric empirical Bayes to quantitatively elucidate the interactions among the left motor cortex (ML), right cerebellum (CBR) and left visual cortex (VL), and their roles as higher and lower controllers in the hierarchical model.

Results

We found that the tracking task with visual feedback strongly affected the modulation of connection strength in ML → CBR and ML↔VL. Moreover, we found that the modulation of VL → ML, ML → ML, and ML → CBR by the tracking task with visual feedback could explain individual differences in tracking performance and muscle activity, and we validated these findings by leave-one-out cross-validation.

Discussion

We demonstrated the effectiveness of our approach for understanding the mechanisms underlying human motor control. Our proposed method may have important implications for the development of new technologies in personalized interventions and technologies, as it sheds light on how different brain regions interact and work together during a motor task.

Pattern Matching for Real-Time Extraction of Fast and Slow Spectral Components From sEMG Signals

Previous studies have demonstrated the potential of surface electromyography (sEMG) spectral decomposition in evaluating muscle performance, motor learning, and early diagnosis of muscle conditions. However, decomposition techniques require large data sets and are computationally demanding, making their implementation in real-life scenarios challenging. Based on the hypothesis that spectral components will present low inter-subject variability, the present paper proposes the foundational principles for developing a real-time system for their extraction by utilizing a pre-defined library of components derived from an extensive data set to match new measurements. The model library was tailored to fulfill specific requirements for real-time system application and the challenges encountered during implementation are discussed in the paper. For system validation, four distinct data sets comprising isotonic and isometric muscle activations were utilized. The extracted during validation showed low inter-subject variability, suggesting that a wide range of physiological variations can be described with them. The adoption of the proposed system for muscle analysis could provide a deeper understanding of the underlying mechanisms governing different motor conditions and neuromuscular disorders, as it allows for the measurement of these components in various daily-life scenarios.