Evaluation of methods for extraction of the volitional EMG in dynamic hybrid muscle activation

BACKGROUND: Hybrid muscle activation is a modality used for muscle force enhancement, in which muscle contraction is generated from two different excitation sources: volitional and external, by means of electrical stimulation (ES). Under hybrid activation, the overall EMG signal is the combination of the volitional and ES-induced components. In this study, we developed a computational scheme to extract the volitional EMG envelope from the overall dynamic EMG signal, to serve as an input signal for control purposes, and for evaluation of muscle forces. METHODS: A "synthetic" database was created from in-vivo experiments on the Tibialis Anterior of the right foot to emulate hybrid EMG signals, including the volitional and induced components. The database was used to evaluate the results obtained from six signal processing schemes, including seven different modules for filtration, rectification and ES component removal. The schemes differed from each other by their module combinations, as follows: blocking window only, comb filter only, blocking window and comb filter, blocking window and peak envelope, comb filter and peak envelope and, finally, blocking window, comb filter and peak envelope. RESULTS AND CONCLUSION: The results showed that the scheme including all the modules led to an excellent approximation of the volitional EMG envelope, as extracted from the hybrid signal, and underlined the importance of the artifact blocking window module in the process. The results of this work have direct implications on the development of hybrid muscle activation rehabilitation systems for the enhancement of weakened muscles

Konzept zur automatisierten Anpassung der neuronalen Schnittstellen bei nichtinvasiven Neuroprothesen

Diese Arbeit widmet sich der Frage der Automatiserung bei der Anpassung der neuronalen Schnittstellen von nichtinvasiven Neuroprothesen bei hochquerschnittgelähmten Personen. Den Schwerpunkt der Arbeit bildet die Entwicklung von Methoden zur optimalen Positionierung der EMG-Sensoren bzw. FES-Elektroden, welche bei einer nichtinvasiven Neuroprothese die sensorischen bzw. motorischen neuronalen Schnittstellen darstellen