Sparse signal processing for neural motor interfaces (PhD)

This project will focus on the study of biologically informed low-dimensional sub-spaces in a variety of movement-related signals (i.e. local field potential (LFP) recordings from motor cortical areas, forearm surface electromyogram (sEMG) signals, finger kinematic signals), and subsequently use these low-dimensional manifolds to perform predictions for a variety of neural engineering applications. The proposed project comprises two major components: i) electromyogram (EMG) signal decoding of upper-limb activity from LFP recordings in primates, and ii) continuous decoding of finger movement kinematic parameters from forearm sEMG recordings in humans.

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