Engineering and Technology

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

Z. Liu, J. Tang, et al.

Explore the groundbreaking memristor-based neural signal analysis system developed by Zhengwu Liu and colleagues, achieving a remarkable 93.46% accuracy in identifying epilepsy-related signals while significantly enhancing power efficiency. This innovative approach promises to redefine brain-machine interfaces for restored motor functions.

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Abstract

Brain-machine interfaces (BMIs) are crucial for restoring lost motor functions and understanding brain mechanisms. However, current BMIs based on von Neumann architecture face limitations in signal processing due to the exponentially increasing number of recording electrodes. This work introduces a memristor-based neural signal analysis system that leverages the bio-plausible characteristics of memristors for efficient analog domain signal processing. Using memristor arrays, the system achieves a 93.46% accuracy in filtering and identifying epilepsy-related neural signals, demonstrating a 400x power efficiency improvement compared to state-of-the-art CMOS systems. This highlights the potential of memristors for high-performance neural signal analysis in next-generation BMIs.

Publisher

Nature Communications

Published On

Aug 25, 2020

Authors

Zhengwu Liu, Jianshi Tang, Bin Gao, Peng Yao, Xinyi Li, Dingkun Liu, Ying Zhou, He Qian, Bo Hong, Huaqiang Wu

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