Brain-machine interactive control (BMIC) of prosthetic limbs for high speed and natural movements is a major challenge. The current BMIC paradigm employs a feedforward interface between the brain and prosthetic, referred to as the "decoder", whose success relies heavily on the ability of the brain to adapt appropriately utilizing visual feedback information in a "certain" environment. Such decoders are trained using data from healthy subjects but are implemented as interfaces for spinal cord patients. The motor cortical output of the healthy subject is substantially different from that of an injured patient, and decoders do not account for spurious signals generated in the cerebellum due to the loss of proprioceptive data. Thus, the key challenge is to design robust decoders for BMIC of the future that take into account both cerebellar and cortical contributions.
Intellectual Merit: We propose a novel Robust Decoder-Compensator (RDC) architecture for interactive control of fast movements in the presence of uncertainty. The RDC is a feedback interconnection that 1) decodes cortical signals to produce actuator commands that reflect motor intent, 2) corrects for spurious cerebellar signals generated in the absence of proprioceptive feedback, and 3) makes robust the interconnection to account for mismatches between models and reality. Multi-site intracranial EEG recordings in motor areas obtained from epilepsy patients executing fast and loaded movements will facilitate system identification of cortical structures in healthy and in spinal cord patients. The cortical models and the RDC architecture will belong to a class of linear parameter varying systems, and the RDC will be synthesized to maintain performance over a wide range of movements and environments. Finally, we will implement the interactive system on patients with implanted electrodes.
Broader Impact: We provide a unified framework aimed at understanding human motor control, which incorporates cerebellar and cortical models and builds a BMIC for fast and natural movements, allowing patients with spinal cord injuries and cerebellar ataxia to execute rapid natural trajectories. Powerful extensions of closed-loop system identification and robust control synthesis techniques for LPV systems will be developed. This program will also give students rare opportunities to address challenges at the interface between systems engineering and neurobiology.
