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iEMG: Imaging electromyography.

J Electromyogr Kinesiol

April 2016

Department of Informatics, Technische Universität München, Munich, Germany; fortiss, TUM Associate Institute, Munich, Germany.

Advanced data analysis and visualization methodologies have played an important role in making surface electromyography both a valuable diagnostic methodology of neuromuscular disorders and a robust brain-machine interface, usable as a simple interface for prosthesis control, arm movement analysis, stiffness control, gait analysis, etc. But for diagnostic purposes, as well as for interfaces where the activation of single muscles is of interest, surface EMG suffers from severe crosstalk between deep and superficial muscle activation, making the reliable detection of the source of the signal, as well as reliable quantification of deeper muscle activation, prohibitively difficult. To address these issues we present a novel approach for processing surface electromyographic data.

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