Functional Ultrasound Imaging-Based Brain-Computer Interface Technologies and Applications

Brain–computer interface (BCI) enables information transmission between the brain and external devices by reading brain activity signals. Its performance is constrained by trade-offs among invasiveness, spatial precision, imaging depth, and long-term robustness of neural interface modalities. Functional ultrasound imaging (fUSI) can sensitively capture whole-brain microvascular dynamics mediated by neurovascular coupling, thereby providing a noninvasive and high-resolution readout of local neural activity. It offers a new pathway for next-generation BCI signal acquisition oriented toward noninvasive applications. Following the theme of “from cerebral blood-flow imaging to neural decoding,” this review systematically summarizes the signal mechanisms, brain function detection and characterization methods, BCI readout feasibility, and core algorithmic frameworks of fUSI. Current studies have demonstrated that fUSI can be applied to fine-scale functional brain mapping, movement intention decoding, and natural behavioral variable analysis, showing strong potential for neural decoding. Future directions include real-time three-dimensional and four-dimensional imaging, multimodal integration, and adaptive online decoding, which may provide new approaches for high-precision closed-loop noninvasive BCI.