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

Zhengwu Liu, Jianshi Tang (), Bin Gao, Peng Yao, Xinyi Li, Dingkun Liu, Ying Zhou, He Qian, Bo Hong () and Huaqiang Wu ()
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Zhengwu Liu: Tsinghua University
Jianshi Tang: Tsinghua University
Bin Gao: Tsinghua University
Peng Yao: Tsinghua University
Xinyi Li: Tsinghua University
Dingkun Liu: Tsinghua University
Ying Zhou: Tsinghua University
He Qian: Tsinghua University
Bo Hong: Tsinghua University
Huaqiang Wu: Tsinghua University

Nature Communications, 2020, vol. 11, issue 1, 1-9

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-18105-4

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