Autonomous robot navigates brain veins for bci device access
Autonomous Robotic Navigation for Endovascular Brain-Computer Interface Access
RoboticsMachine Learning
Summary
Delivering brain-computer interface (BCI) devices inside the brain without large surgery is tricky because veins vary a lot between people. The authors trained a robot to navigate brain venous pathways autonomously using computer simulations and then tested it on physical models. The robot was able to reach target veins successfully most of the time, even in new anatomical setups it had never seen before. They also developed a system that predicts when the robot might fail, which could help humans supervise the robot safely. The study shows this approach is promising but needs improvements before real medical use.
What this means in practice
- •For medical device engineers: Develop autonomous robotic systems to deliver brain implants through veins safely without open brain surgery.$Commercial implications: Enables design of robotic devices for minimally invasive brain-computer interface placement, an unmet clinical need in neurotechnology product markets.
- •For robotic system developers: Use machine learning control and online failure prediction to improve robotic navigation in complex, varied anatomy.
Authors
Harry Robertshaw, Weijie Qi, Nikola Fischer, Alejandro Granados, Thomas C. Booth, Sam E. John
Abstract
Endovascular brain-computer interfaces (BCIs) avoid craniotomy but require precise device delivery through anatomically variable cerebral veins. This work presents the first demonstration of in vitro autonomous robotic navigation for endovascular BCI access in the cerebral venous system. Soft Actor-Critic controllers were trained in silico for two sequential tasks spanning the right internal jugular vein to the superior sagittal sinus, using geometric augmentation of one training anatomy. Navigation was evaluated in a training anatomy and an anatomically unseen hold-out model over 250 in silico episodes and five fluoroscopy-guided in vitro robotic runs per task-anatomy condition, comprising 1,000 simulated episodes and 20 physical runs overall. Task recurrent predictors were also evaluated for online identification of impending navigation failure. In silico success rates for Tasks A and B were 85.6% and 98.4% in the training anatomy and 42.0% and 91.6% in the hold-out anatomy, respectively. Fourteen of 20 physical runs were successful (70% overall), including 80% success for Task B in the hold-out phantom. In silico the predictors detected 99.3-100.0% of failures with false-alarm rates of 0.8-6.7%. During in vitro evaluation, predicted risk increased before failed episodes, but elevated probabilities during some successful runs showed reduced calibration after transfer. These results demonstrate the feasibility of autonomous cerebral venous access and show how online failure prediction could support human oversight, while also identifying anatomical generalization and sim-to-real calibration as priorities before preclinical translation.