Performance-Recoverable Closed-Loop Neuroprosthetic System.

Journal: Advanced materials (Deerfield Beach, Fla.)
Published Date:

Abstract

Soft bioelectronics mechanically comparable to living tissues have driven advances in closed-loop neuroprosthetic systems for the recovery of sensory-motor functions. Despite notable progress in this field, critical challenges persist in achieving long-term stable closed-loop neuroprostheses, particularly in preventing uncontrolled drift in the electrical sensitivity and/or charge injection performance owing to material fatigue or mechanical damage. Additionally, the absence of an intelligent feedback loop has limited the ability to fully compensate for sensory-motor function loss in nervous systems. Here, a novel class of soft, closed-loop neuroprosthetic systems is presented for long-term operation, enabled by spontaneous performance recovery and machine-learning-driven correction to address the material fatigue inherent in chronic wear or implantation environments. Central to this innovation is the development of a tough, self-healing, and stretchable bilayer material with high conductivity and exceptional cyclic durability employed for robot-interface touch sensors and peripheral-nerve-adaptive electrodes. Furthermore, two central processing units, integrated in a prosthetic robot and an artificial brain, support closed-loop artificial sensory-motor operations, ensuring accurate sensing, decision-making, and feedback stimulation processes. Through these characteristics and seamless integration, our performance-recoverable closed-loop neuroprosthesis addresses challenges associated with chronic-material-fatigue-induced malfunctions, as demonstrated by successful in vivo under 4 weeks of implantation and/or mechanical damage.

Authors

  • Yewon Kim
    Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Republic of Korea.
  • Kyumin Kang
    Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, 16419, Republic of Korea.
  • Ja Hoon Koo
    Department of Semiconductor Systems Engineering and Institute of Semiconductor and System IC, Sejong University, Seoul, Republic of Korea.
  • Yoonyi Jeong
    Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, 16419, Republic of Korea.
  • Sungjun Lee
    Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Republic of Korea.
  • Dongjun Jung
    Center for Nanoparticle Research, IBS, Seoul, 08826, Republic of Korea.
  • Duhwan Seong
    Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
  • Hyeok Kim
    School of Electrical and Computer Engineering, Center for Smart Sensor System of Seoul (CS4), University of Seoul, 163 Seoulsiripdaero, Dongdaemun-gu, Seoul, 02504, Republic of Korea.
  • Hyung-Seop Han
    Center for Biomaterials, Biomedical Research Division, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea.
  • Minah Suh
    Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, 16419, Republic of Korea.
  • Dae-Hyeong Kim
    School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea.
  • Donghee Son
    Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, Republic of Korea.

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