Decoding Muscle Excitation Primitives from Slow Cortical Potentials During Knee Flexion-Extension

Abstract

Linear decoders have been successfully applied to extract human limbs kinematics from low-frequency cortical modulations. In this, intermediate descending motor pathways are absorbed in the regression. Here we propose the use of linear decoders to map cortical function to the spinal function (muscle primitive-level), thus shortening the transmission distance and reducing the dimensionality of the decoding of a large number of muscles. Our first results show that it is possible to accurately reconstruct muscle primitives computed from knee flexion-extension and to successfully detect muscle activity during repetitive cyclic movements.