Neural signal analysis with memristor arrays towards high-efficiency brain-machine interfaces
Neural signal analysis with memristor arrays towards high-efficiency brain-machine interfaces
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DOI：
10.1038/s41467-020-18105-4
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发表时间：
2020-08-25
影响因子：
16.6
通讯作者：
Wu, Huaqiang
中科院分区：
文献类型：
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作者：
Liu, Zhengwu;Tang, Jianshi;Wu, Huaqiang
脑-机接口是恢复失去的运动功能和探索大脑功能机制的有前途的工具。随着记录电极的数量呈指数级增长，脑机接口的信号处理能力正在落后。其中一个关键的瓶颈是，它们采用了传统的冯·诺依曼架构，带有数字计算，与人脑的工作原理有着根本的不同。在这项工作中，我们提出了一个基于忆阻器的神经信号分析系统，其中忆阻器的生物似然特性被用来分析信号在模拟域中具有高效率。作为概念验证演示，忆阻器阵列用于实现癫痫相关神经信号的滤波和识别，实现了93.46%的高准确度。值得注意的是，与最先进的互补金属氧化物半导体系统相比，我们基于忆阻器的系统显示出近400倍的功率效率改进。这项工作证明了在下一代脑机接口中使用忆阻器进行高性能神经信号分析的可行性。
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 400x 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.