Motor-Control

How does the brain communicate with muscles during unexpected perturbations, and how does this change with age? We investigated corticomuscular connectivity during perturbed recumbent stepping in young and older adults using high-density EEG and EMG. Young adults demonstrated selective connectivity between error-processing brain regions and specific muscles, with strong involvement of the anterior cingulate cortex. In contrast, older adults showed elevated baseline connectivity and relied on diffuse patterns dominated by motor and posterior parietal cortices, connecting to multiple muscles simultaneously regardless of their biomechanical role. This reveals a strategic reorganization: young adults use dynamic, error-driven control, while older adults employ a stability-focused approach that maintains comparable performance through constitutive hyperconnectivity. These distinct connectivity signatures establish perturbed recumbent stepping as a valuable tool for assessing age-related sensorimotor changes and developing targeted rehabilitation interventions.

The Hyser dataset provides 256-channel HD-sEMG recordings from 20 subjects across five distinct tasks, including gesture recognition and force control paradigms, making it ideal for developing advanced neural interfaces and prosthetic control algorithms.

Older adults often demonstrate greater co-contraction and motor errors than young adults in response to motor perturbations. We demonstrated that older adults reduced their motor errors more than young adults with brief perturbations during recumbent stepping while maintaining greater muscle co-contraction. In doing so, older adults largely used one muscle pair to drive the stepper, tibialis anterior and soleus, while young adults used all muscles. These two muscles are crucial for maintaining upright balance.

We show that the effects of fatigue on muscle coordination and neural drive can be reliably characterized using a non-parametric functional muscle network. The network demonstrated a consistent decrease in connectivity after the fatigue intervention, as indicated by network degree, weighted clustering coefficient (WCC), and global efficiency. The graph metrics displayed consistent and significant decreases at the group level, individual subject level, and individual muscle level. The proposed approach has the potential to be a sensitive biomarker of fatigue with superior performance to conventional spectrotemporal measures.

We show that InfoMuNet, a novel functional biomarker based on a nonlinear network graph of muscle connectivity, can quantify the role of sensory information on motor performance. We demonstrate its potential use in precision rehabilitation interventions.

This project explores how sensory feedback can enhance motor control during desk cycling, with applications in rehabilitation and workplace wellness. The research focuses on optimizing user experience and therapeutic benefits through intelligent feedback systems.

We demonstrate that seated locomotor perturbations produce differential theta-band responses in the anterior cingulate and supplementary motor areas, suggesting that tuning perturbation parameters can potentially modify electrocortical responses.