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Motor Cortex Coverage Predicts Signal Strength of a Stentrode Endovascular Brain-Computer Interface.

Hunter R Schone^1,2, Peter Yoo³, Adam Fry³

¹Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, USA.

Medrxiv : the Preprint Server for Health Sciences

|October 3, 2025

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This summary is machine-generated.

Brain-computer interfaces (BCIs) using the Stentrode implant show variable signal strength. Optimal placement overlapping the primary motor cortex (M1) is key for stronger neural signals and better device control.

Area of Science:

Neuroscience
Biomedical Engineering
Assistive Technology

Background:

Brain-computer interfaces (BCIs) offer assistive control for motor impairments.
The Stentrode BCI records neural signals from within the brain's vasculature.
Significant variability in Stentrode BCI signal strength exists across users.

Purpose of the Study:

Investigate predictors of Stentrode BCI motor signal strength.
Identify factors influencing BCI performance in individuals with motor impairments.
Enhance future BCI control success through user-specific factor analysis.

Main Methods:

Analyzed data from 10 participants implanted with Stentrode BCIs over 5 years.
Assessed clinical status, pre-implant function, neuroanatomy, vasculature, and device integrity.

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Correlated user-specific factors with longitudinal Stentrode BCI motor signal strength.

Main Results:

The strongest predictor of signal strength was the Stentrode's overlap with the primary motor cortex (M1).
User-specific factors significantly influence BCI performance.
Observed inter-participant variability in signal acquisition.

Conclusions:

Targeting M1 during Stentrode deployment is crucial for maximizing signal strength.
A scientific framework is provided for understanding user-specific influences on BCI outcomes.
Findings inform strategies for improving BCI efficacy in assistive technology.