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

• Published in: Nature communications Vol. 11; no. 1; p. 4234
• Main Authors: Liu, Zhengwu, Tang, Jianshi, Gao, Bin, Yao, Peng, Li, Xinyi, Liu, Dingkun, Zhou, Ying, Qian, He, Hong, Bo, Wu, Huaqiang
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 25.08.2020; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/985; 639/166/987; Arrays; Brain; Digital computers; Efficiency; Electrodes; Energy efficiency; Epilepsy; Filtration; Functionals; Humanities and Social Sciences; Interfaces; Man-machine interfaces; Memristors; Metal oxides; multidisciplinary; Power efficiency; Recording; Science; Science (multidisciplinary); Signal analysis; Signal generation; Signal processing
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-020-18105-4

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. Designing energy efficient and high performance brain-machine interfaces with millions of recording electrodes for in-situ analysis remains a challenge. Here, the authors develop a memristor-based neural signal analysis system capable of filtering and identifying epilepsy-related brain activities with an accuracy of 93.46%.