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.
Highlights
Brain-machine interfaces are promising tools to restore lost motor functions and probe brain functional mechanisms
Owing to the excellent I–V linearity and analog switching behaviors of our memristors, the system achieves a high accuracy over 93.46% while showing more than two orders of magnitude advantage in power efficiency compared to state-of-the-art complementary metal-oxide-semiconductor (CMOS) systems
The memristor array has the central role in such a Brain–machine interfaces (BMIs) as it translates the neural signals into control commands for the external effectors, such as a prosthesis or a mouse
Summary
Brain-machine interfaces are promising tools to restore lost motor functions and probe brain functional mechanisms. The signal processing modules in most existing BMIs are based on silicon-based complementary metal-oxide-semiconductor (CMOS) technology and adopt the conventional von Neumann architecture where memory and data computing units are physically separated They usually first convert analog neural signals to digital signals and compress[10,11] and process them in the digital domain[9,12] using various application-specific integrated circuits (ASICs). The design of such systems is still facing many challenges, such as power budget, delay and scalability, especially in order to catch up with the exponentially increasing number of recording sites in state-of-the-art neural probes[12,13,16,17,18] This conventional approach is fundamentally different from how brain processes information that is in analog and continuous fashion. Owing to the excellent I–V linearity and analog switching behaviors of our memristors, the system achieves a high accuracy over 93.46% while showing more than two orders of magnitude advantage in power efficiency compared to state-of-the-art CMOS systems
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