NCS-FO: Integrating neural interfaces and machine intelligence for advanced neural prosthetics

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Brain-machine interfaces (BMI) read signals directly from the brain to control external devices such as robotic limbs. While this technology has great potential to benefit people who are paralyzed, BMIs often have poor performance because they use noisy, low-level signals to simultaneously control many aspects of the robotic limb's movements. In contrast, this project will address this shortcoming by reading high-level intents from the brain in order to control an intelligent robotic system. These changes reflect cutting-edge advances in neuroscience and machine intelligence and will require close cooperation between scientists, engineers, and physicians. The project aims to leverage expertise across these diverse fields in order to generate significant improvements in BMI technology to advance the national health, increase scientific understanding of the brain, and lead to dramatic improvements in the quality of life for these severely disabled persons.This collaborative project will decode high-level cognitive actions from neural signals recorded in the parietal cortex of a tetraplegic human, then carry out those intents using a smart robotic prosthesis. Persons with tetraplegia who have multielectrode arrays (MEA) implanted in reach and grasp areas of the posterior parietal cortex (PPC), will participate in experiments to explore the neural representation of cognitive intentions in human PPC including object selection, action intention, and neural control of robotic limbs. Experimental results will be used to construct BMI control algorithms optimized to decode these cognitive signals. In parallel, a modular, semi-autonomous robotic prosthesis will be developed that can identify household objects and plan reach-and-grasp movements to manipulate or transport the objects. These scientific and technological efforts will be supported by continued clinical care of the tetraplegic participants. The study will explore increasingly capable iterations of the BMI system, culminating in testing of the fully developed BMI system in the participants' own home environment where they will practice activities of daily living. The resulting system will leverage deep insights in cognitive neuroscience and advanced capabilities in machine sensing and robotic control systems to substantially improve the ease of use and capability of brain-machine interfaces.