Autonomous Robotic Navigation for Endovascular Brain-Computer Interface Access
This arXiv preprint reports the first demonstration of autonomous robotic navigation through cerebral veins for endovascular brain-computer interface access. Two Soft Actor-Critic tasks were trained in simulation and tested on 250 simulated and five physical fluoroscopy-guided runs per condition; overall in vitro success was 70% (14/20), but hold-out Task A fell to 40–42% and the failure predictor lost calibration in physical tests. The work establishes feasibility for reducing operator burden in BCI delivery, though substantial validation remains before clinical use.
Could an autonomous robotic system navigate the cerebral venous system from the right internal jugular vein to the superior sagittal sinus, and could an online recurrent predictor flag impending failures for human-supervised autonomy?
Endovascular brain-computer interface placement requires navigating a catheter and guidewire through tortuous, patient-specific cerebral veins to a precise target; manual fluoroscopy is skill-dependent and exposes operators to radiation. Existing autonomous endovascular research has focused on arterial and cardiac circulation, leaving the cerebral venous access route unaddressed.
Earlier methods used manual neurointerventional technique or reinforcement learning for cardiac, aortic, coronary, and some arterial cerebral navigation. These systems were not designed for the cerebral venous anatomy or the superior sagittal sinus access route, and they did not typically combine autonomous venous navigation with online failure prediction for BCI placement.
The authors trained recurrent Soft Actor-Critic policies in the SOFA/stEVE simulator using one MRI-derived venous model with geometric augmentation, covering two sequential tasks from the right internal jugular vein to the inferior and superior superior sagittal sinus. A separate task-specific GRU failure predictor was trained post hoc on saved navigation trajectories to estimate failure probability within the next 20 control steps. The learned policy and predictor were then transferred to 3D-printed PLA phantom anatomies with a robotic catheter/guidewire manipulator under fluoroscopic tip tracking; persistent high-risk predictions could trigger automatic guidewire retraction in a proof-of-concept mode.
In simulation over 250 episodes per condition, Task A/Task B success was 85.6%/98.4% in the training anatomy and 42.0%/91.6% in the hold-out anatomy. Across 20 physical runs (five per condition), overall success was 70% (14/20): Task A was 80% in the training phantom and 40% in the hold-out phantom, while Task B was 80% in both. Simulated failure predictors detected 99.3–100.0% of failures, with false-alarm rates of 0.8–6.7% and median lead times of 22–27 steps; in physical testing predicted risk also increased during successful runs, showing reduced calibration after transfer. Mean in silico procedure time for successful runs was 10.33–13.40 s, while in vitro mean times ranged from 85 to 188 s.
The authors state only two venous anatomies were available, with one used for training and one as hold-out; only five physical runs per condition; no manual expert benchmark was included; rigid PLA phantoms lacked venous compliance, physiological flow, and motion, and friction caused several failures. The study measured access only—no BCI device was deployed, no neural signals recorded, and no contact-force or vessel-wall-safety outcomes measured. The predictor was trained on trajectories from the same controller family and was not recalibrated on physical data; automatic retraction was not compared with a no-retraction control. No code or dataset release is mentioned in the paper.
This could inform companies developing endovascular BCI systems, such as Synchron, and robotic neurointerventional platforms by showing that learned navigation plus human oversight may eventually reduce operator burden and improve targeting consistency. In the near term, the results support research and phantom/simulator development; clinical or commercial use is likely 3+ years away and would require larger patient-derived venous datasets, compliant phantoms and preclinical validation, physical calibration of failure predictors, force-aware safety constraints, and evidence that autonomous control is non-inferior or superior to expert manual navigation.
The full text is not republished here because the paper's license does not allow it. Read the original on arXiv.