Artificial Intelligence Enables Real-Time and Intuitive Control of Prostheses via Nerve Interface

Diu Khue Luu, Anh Tuan Nguyen, Ming Jiang, Markus W Drealan, Jian Xu, Tong Wu, Wing kin Tam, Wenfeng Zhao, Brian Z.H. Lim, Cynthia K. Overstreet, Qi Zhao, Jonathan Cheng, Edward Keefer, Zhi Yang

Research output: Contribution to journalArticlepeer-review


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 amputees 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.

Original languageEnglish (US)
Pages (from-to)3051-3063
Number of pages13
JournalIEEE Transactions on Biomedical Engineering
Issue number10
StatePublished - Oct 1 2022

Bibliographical note

Publisher Copyright:


  • Artificial intelligence
  • Decoding
  • Deep learning
  • Electromyography
  • Implants
  • Prosthetic hand
  • Real-time systems
  • artificial intelligence
  • deep learning
  • information throughput
  • information transfer rate
  • motor decoding
  • neural decoder
  • neuroprosthesis
  • peripheral nerve
  • reaction time

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't


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