Browsing by Subject "human-machine symbiosis"

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    A High-Precision Bioelectric Neural Interface Toward Human-Machine Symbiosis
    (2021-01) Nguyen, Anh Tuan
    Objective. A symbiosis of human intelligence and artificial intelligence (AI) cannot be achieved without establishing an intuitive, bidirectional, and high-bandwidth information conduit between the minds and machines. Approach. Here we focus on developing high-precision bioelectronics underlying a new class of bioelectric neural interfaces that could bring us one step closer to this feat. We pioneer new circuit techniques, including frequency shaping (FS), redundant sensing (RS), RS-based super-resolution, and redundant crossfire (RXF), to enhance the effective resolution of neural recording and stimulation. These fundamentals allow the implementation of a series of fully-integrated microchips called Neuronix capable of acquiring low-noise neural signals and delivering high-precision electrical microstimulation. The Neuronix chips are incorporated to create miniaturized neuromodulation devices, including the Scorpius system, to enable bidirectional communications with neural circuits. Results. In a clinical study with human amputees, the Scorpius system helps establish a peripheral nerve interface that allows deep learning-based AI models to read and decode the patients' intents of moving individual fingers. Our analysis of acquired electroneurography (ENG) signals demonstrates this robust nerve interface has a sufficient information capacity to enable real-time control of a multi-degree-of-freedom (DOF) neuroprosthetic hand with near-natural dexterity and intuitiveness while simultaneously delivering somatosensory feedback. Significance. Our study layouts the principled foundation toward not only a dexterous control strategy for advanced neuroprostheses but also an intuitive conduit for connecting the human minds and machines. This opens up possibilities for many biomedical applications and manifests the basis of the future human-machine symbiosis.