What if MRI could do more than image the brain? The system combines imaging, decoding, stimulation, and evaluation in one loop.
Shanghai United Imaging Healthcare and Tianjin University have jointly released what they describe as the world’s first full-stack magnetic resonance imaging (MRI)-based brain-computer interface (BCI) solution called uMR Shenguan. The system integrates signal acquisition, decoding, brain modulation, and effect evaluation within a single technical framework.
The development targets a key challenge in combining MRI with BCI hardware. Conventional MRI systems can face interference from implanted or external BCI devices, while BCI systems need accurate information about brain activity and structure to support research and clinical applications.
The solution is designed to create a closed loop in which MRI can observe brain activity, support modulation, and verify the resulting effects. This shifts MRI from being primarily an imaging system towards acting as a perception and feedback component in BCI applications.
The system combines millisecond-level capture of neural activity with sub-millimetre measurement of brain structures. It also includes technologies intended to compensate for magnetic-field distortions caused by BCI devices, along with a magnetically compatible BCI toolbox designed to reduce interference between MRI equipment and interface hardware.
Its technical framework covers high-spatiotemporal-resolution MRI, MRI hardware adaptation and optimisation, BCI adaptation for magnetic environments, and MRI-guided neuromodulation. These capabilities are intended to support more detailed analysis of how brain structure and function respond to intervention.
The development comes as BCI technologies move from laboratory research towards clinical and industrial applications. Potential medical areas include motor rehabilitation, neuro-critical care, psychiatry, ophthalmology, and audiology, while future consumer applications could include education, sports, gaming, sleep improvement, and industrial safety.
“The solution enables synchronized coordination of observation and intervention in brain research, advancing brain-computer interfaces from ‘decoding signals’ toward ‘understanding the brain’,” says Ming Dong, Vice President of Tianjin University and Director of the Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration.
