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<p>Practical brain–computer interfaces should be able to decipher brain signals and dynamically adapt to brain fluctuations. This, however, requires a decoder capable of flexible updates with energy-efficient decoding capabilities. Here we report a neuromorphic and adaptive decoder for brain–computer interfaces, which is based on a 128k-cell memristor chip. Our approach features a hardware-efficient one-step memristor decoding strategy that allows the interface to achieve software-equivalent decoding performance. Furthermore, we show that the system can be used for the real-time control of a drone in four degrees of freedom. We also develop an interactive update framework that allows the memristor decoder and the changing brain signals to adapt to each other. We illustrate the capabilities of this co-evolution of the brain and memristor decoder over an extended interaction task involving ten participants, which leads to around 20% higher accuracy than an interface without co-evolution.</p>

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A memristor-based adaptive neuromorphic decoder for brain–computer interfaces

  • 期刊论文

  • 发表日期：

    2025年2月17日

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  • Zhengwu Liu,
  • Jie Mei,
  • Jianshi Tang,
  • Minpeng Xu,
  • Bin Gao,
  • Kun Wang,
  • Sanchuang Ding,
  • Qi Liu,
  • Qi Qin,
  • Weize Chen,
  • Yue Xi,
  • Yijun Li,
  • Peng Yao,
  • Han Zhao,
  • Ngai Wong,
  • He Qian,
  • Bo Hong,
  • Tzyy-Ping Jung,
  • Dong Ming,
  • Huaqiang Wu

摘要

Practical brain–computer interfaces should be able to decipher brain signals and dynamically adapt to brain fluctuations. This, however, requires a decoder capable of flexible updates with energy-efficient decoding capabilities. Here we report a neuromorphic and adaptive decoder for brain–computer interfaces, which is based on a 128k-cell memristor chip. Our approach features a hardware-efficient one-step memristor decoding strategy that allows the interface to achieve software-equivalent decoding performance. Furthermore, we show that the system can be used for the real-time control of a drone in four degrees of freedom. We also develop an interactive update framework that allows the memristor decoder and the changing brain signals to adapt to each other. We illustrate the capabilities of this co-evolution of the brain and memristor decoder over an extended interaction task involving ten participants, which leads to around 20% higher accuracy than an interface without co-evolution.