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.
We explored the potential of a functional muscle network to differentiate vocal tasks. The network robustly differentiated vocal tasks, while classic muscle activation assessment failed to differentiate. The study also discovered tasks with the highest network involvement, which may be utilized in the future to monitor voice disorders and rehabilitation.
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.
Download Paper Code and data Abstract Electroencephalography (EEG) and source estimation can be used to identify brain areas activated during a task, which could offer greater insight on cortical dynamics. Source estimation requires knowledge of the locations of the EEG electrodes. This could be provided with a template or obtained by digitizing the EEG electrode locations. Operator skill and inherent uncertainties of a digitizing system likely produce a range of digitization reliabilities, which could affect source estimation and the interpretation of the estimated source locations....
Mismarking fiducial locations can systematically change EEG source locations. We inestigated this effect by systematically moving the fiducial locations to simulate such errors.