  Objective: The next generation prosthetic hand that moves and feels like a
real hand requires a robust neural interconnection between the human minds and
machines. Methods: Here we present a neuroprosthetic system to demonstrate that
principle by employing an artificial intelligence (AI) agent to translate the
amputee's movement intent through a peripheral nerve interface. The AI agent is
designed based on the recurrent neural network (RNN) and could simultaneously
decode six degree-of-freedom (DOF) from multichannel nerve data in real-time.
The decoder's performance is characterized in motor decoding experiments with
three human amputees. Results: First, we show the AI agent enables amputees to
intuitively control a prosthetic hand with individual finger and wrist
movements up to 97-98% accuracy. Second, we demonstrate the AI agent's
real-time performance by measuring the reaction time and information throughput
in a hand gesture matching task. Third, we investigate the AI agent's long-term
uses and show the decoder's robust predictive performance over a 16-month
implant duration. Conclusion & significance: Our study demonstrates the
potential of AI-enabled nerve technology, underling the next generation of
dexterous and intuitive prosthetic hands.
