Brain-machine interfaces are promising tools for rehabilitation medicine and medical electronics. Most conventional neural signal analysis systems use brain-computer interfaces composed of silicon Complementary Metal–Oxide–Semiconductor (CMOS) circuits. However, with the increasing number of recording electrodes, the systems face great challenges in terms of power consumption and delays.
In partnership with researchers from Tsinghua’s School of Medicine, Professor Wu Huaqiang of the Department of Microelectronics and Nanoelectronics and his team have developed a memristor-based neural signal analysis system and used it to implement the filtering and identification of epilepsy-related neural signals, achieving an accuracy rate of over 93 percent. The power consumption of the system is less than one four-hundredth of that of conventional neural signal analysis systems.
“Memristors are a new type of information processing device. Their working mechanism is similar to that of synapses and neurons in the human brain. With their low power consumption, memristors have promising prospects for future data storage and neuromorphic computing,” Wu Huaqiang said.
The research demonstrates the feasibility of using memristors for high-performance neural signal analysis in next-generation brain-machine interfaces. It was published in the journal Nature Communications.
Source: Guo Ying, xinhuanet.com
Editors: Lin Lu, Nico Gous, John Olbrich
