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Back to basics: Advancing the state of brain-machine interfaces


J. DiGiovanna

BioRoNZ: BioRobotics Network Zurich

Brain-machine interfaces create new pathways to allow the nervous system to control artificial devices. There has been active research in this area for decades by many groups around the world. However, clinical implementations have been limited by problems intrinsic to prior learning architectures. In this talk, I will introduce the current BMI control paradigm and explain the advantages of shifting to a state-based control scheme. This hierarchical control allows the brain to make ‘high-level’ decisions (e.g. walk) while lower-level controllers (e.g. spinal cord) manage the details of limb actuation. Identifying useful neuronal states can be a difficult task, but is possible via non-linear projections and reinforcement learning algorithms. Additionally, these state-based controllers allow co-adaptation between the artificial intelligence and the user. Such co-adaptation potentially will improve BMI controller robustness.


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