Researchers propose an adaptive neuromorphic decoder supporting brain-machine co-evolution.
Professor Ngai Wong and Dr Zhengwu Liu from the Department of Electrical and Electronic Engineering at the Faculty of Engineering at the University of Hong Kong (HKU), in collaboration with research teams at Tsinghua University and Tianjin University, have conducted groundbreaking research on memristor-based brain-computer interfaces (BCIs). Published in Nature Electronics, this research presents an innovative approach for implementing energy-efficient adaptive neuromorphic decoders in BCIs that can effectively co-evolve with changing brain signals.
A brain-computer interface (BCI) is a computer-based system that creates a direct communication pathway between the brain and external devices, such as computers, allowing individuals to control these devices or applications purely through brain activity, bypassing the need for traditional muscle movements or the nervous system. This technology holds immense potential across a wide range of fields, from assistive technologies to neurological rehabilitation. However, traditional BCIs still face challenges.
"The brain is a complex dynamic system with signals that constantly evolve and fluctuate. This poses significant challenges for BCIs to maintain stable performance over time," said Professor Wong and Dr Liu. "Additionally, as brain-machine links grow in complexity, traditional computing architectures struggle with real-time processing demands."
The collaborative research addressed these challenges by developing a 128K-cell memristor chip that serves as an adaptive brain signal decoder. The team introduced a hardware-efficient one-step memristor decoding strategy that significantly reduces computational complexity while maintaining high accuracy. Dr Liu, a Research Assistant Professor in the Department of Electrical and Electronic Engineering at HKU, contributed as a co-first author to this groundbreaking work.
In real-world testing, the system demonstrated impressive capabilities in a four-degree-of-freedom drone flight control task, achieving 85.17% decoding accuracy—equivalent to software-based methods—while consuming 1,643 times less energy and offering 216 times higher normalised speed than conventional CPU-based systems.
Most significantly, the researchers developed an interactive update framework that enables the memristor decoder and brain signals to adapt to each other naturally. This co-evolution, demonstrated in experiments involving ten participants over six-hour sessions, resulted in approximately 20% higher accuracy compared to systems without co-evolution capability.
"Our work on optimising the computational models and error mitigation techniques was crucial to ensure that the theoretical advantages of memristor technology could be realised in practical BCI applications," explained Dr Liu. "The one-step decoding approach we developed together significantly reduces both computational complexity and hardware costs, making the technology more accessible for a wide range of practical scenarios."
Professor Wong further emphasised, "More importantly, our interactive updating framework enables co-evolution between the memristor decoder and brain signals, addressing the long-term stability issues faced by traditional BCIs. This co-evolution mechanism allows the system to adapt to natural changes in brain signals over time, greatly enhancing decoding stability and accuracy during prolonged use."
Building on the success of this research, the team is now expanding their work through a new collaboration with HKU Li Ka Shing Faculty of Medicine and Queen Mary Hospital to develop a multimodal large language model for epilepsy data analysis.
"This new collaboration aims to extend our work on brain signal processing to the critical area of epilepsy diagnosis and treatment," said Professor Wong and Dr Liu. "By combining our expertise in advanced algorithms and neuromorphic computing with clinical data and expertise, we hope to develop more accurate and efficient models to assist epilepsy patients."
The research represents a significant step forward in human-centred hybrid intelligence, which combines biological brains with neuromorphic computing systems, opening new possibilities for medical applications, rehabilitation technologies, and human-machine interaction.
The project received support from the RGC Theme-based Research Scheme (TRS) project T45-701/22-R, the STI 2030-Major Projects, the National Natural Science Foundation of China, and the XPLORER Prize.
The research was recently published in an article titled "A Memristor-based Adaptive Neuromorphic Decoder for Brain-computer Interfaces" in Nature Electronics. For details about the research article, please visit:
https://www.nature.com/articles/s41928-025-01340-2
Link to the video demo:
https://assets-eu.researchsquare.com/files/rs-3966063/v1/7a84dc7037b11bad96ae0378.mp4
About Professor Ngai Wong
Prof. Ngai Wong is an Associate Professor in the Department of Electrical and Electronic Engineering at HKU. Since November 2022, he has served as the Project Coordinator for a 5-year RGC Theme-based Research Scheme (TRS) project titled "ReRACE: ReRAM AI Chips on the Edge," leading a team of over 15 researchers from multiple universities to develop cutting-edge compute-in-memory chips and systems. Prof. Wong is an advocate for university-industry collaboration and the practical application of AI technologies. He is the Director of a Joint Lab funded by AVNET, a Fortune 500 company, located at the Data Technology Hub (DT Hub) in Tseung Kwan O, InnoPark. His computer vision-based AI models for image and video enhancement have been licensed by TCL, the world's second-largest TV manufacturer.
About Dr Zhengwu Liu
Dr. Zhengwu Liu is a Research Assistant Professor in the Department of Electrical and Electronic Engineering at HKU. His research focuses on memristor-based compute-in-memory technology and its applications in brain-computer interfaces, signal processing and AI. His work has been published in important journals and conferences including Nature Electronics, Nature Communications, Science Advances, DAC, IEDM. He has been granted 9 patents. He serves as Principal Investigator of the NSFC Young Scientists Fund. He earned his Ph.D. from Tsinghua University (2023) and B.E. from the University of Electronic Science and Technology of China (2018).
