Neural signal analysis with memristor arrays towards high-efficiency brain-machine interfaces.

Journal: Nature communications
PMID: 32843643

Abstract

Brain-machine interfaces are promising tools to restore lost motor functions and probe brain functional mechanisms. As the number of recording electrodes has been exponentially rising, the signal processing capability of brain-machine interfaces is falling behind. One of the key bottlenecks is that they adopt conventional von Neumann architecture with digital computation that is fundamentally different from the working principle of human brain. In this work, we present a memristor-based neural signal analysis system, where the bio-plausible characteristics of memristors are utilized to analyze signals in the analog domain with high efficiency. As a proof-of-concept demonstration, memristor arrays are used to implement the filtering and identification of epilepsy-related neural signals, achieving a high accuracy of 93.46%. Remarkably, our memristor-based system shows nearly 400× improvements in the power efficiency compared to state-of-the-art complementary metal-oxide-semiconductor systems. This work demonstrates the feasibility of using memristors for high-performance neural signal analysis in next-generation brain-machine interfaces.

Authors

  • Zhengwu Liu
    Institute of Microelectronics, Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China.
  • Jianshi Tang
    Institute of Microelectronics, Beijing Innovation Center for Future Chips (ICFC), Tsinghua University, Beijing, 100084, China. jtang@tsinghua.edu.cn.
  • Bin Gao
    Institute of Microelectronics, Tsinghua University, Beijing, 10084, China; Center for Brain-Inspired Computing Research, Tsinghua University, Beijing, 10084, China. Electronic address: gaob1@tsinghua.edu.cn.
  • Peng Yao
    Institute of Microelectronics, Tsinghua University, Beijing, 10084, China. Electronic address: yaop14@mails.tsinghua.edu.cn.
  • Xinyi Li
    Department of Radiation Oncology, Duke University Medical Center, Durham, NC, United States.
  • Dingkun Liu
    Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China.
  • Ying Zhou
    Institute of Drug Metabolism and Pharmaceutical Analysis, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
  • He Qian
    Institute of Microelectronics, Tsinghua University, Beijing, 10084, China; Center for Brain-Inspired Computing Research, Tsinghua University, Beijing, 10084, China. Electronic address: qianh@tsinghua.edu.cn.
  • Bo Hong
    Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China. hongbo@tsinghua.edu.cn.
  • Huaqiang Wu
    Institue of Microelectronics, Tsinghua University, Beijing, 100084, China.

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