Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Optical Tag-Based Neuronavigation and Augmentation System for Non-Invasive Brain Stimulation

Xuyi Hu, Ke Ma, Siwei Liu, Per Ola Kristensson, Stefan Goetz

TL;DR

This work addresses the need for accessible, precise neuronavigation in transcranial magnetic stimulation by introducing a low-cost optical-tag tracking system that uses multiple consumer cameras to track the TMS coil and patient head in real time. A Unity-based 3D brain model and AR overlays provide intuitive, in-situ visualization to guide coil placement, reducing reliance on abstract numerical displays. The approach combines AprilTag-based pose estimation, multi-camera fusion with Gaussian error combination, and a smartphone AR interface, achieving sub-millimeter depth precision and sub-degree orientation accuracy. The solution offers a practical path to broader adoption of TMS neuronavigation by lowering cost and complexity while maintaining competitive accuracy and usability.

Abstract

Accurate neuronavigation is critical for effective transcranial magnetic stimulation (TMS), as stimulation outcomes depend directly on precise coil placement. Existing neuronavigation systems are often costly, complex, and prone to tracking errors. To address these limitations, we present a computer vision based neuronavigation system that enables real time tracking of the patient and TMS instrumentation. The system integrates a multi camera optical tracking setup with consumer grade hardware and visible markers to drive a digital twin of the stimulation process. A dynamic 3D brain model in Unity updates in real time to visualize coil position and estimated stimulation targets. Augmented reality (AR) is further incorporated to project this model directly onto the patient's head, enabling intuitive, in situ coil adjustment without reliance on abstract numerical displays. Overall, the proposed approach improves spatial precision and accuracy while enhancing usability.

Optical Tag-Based Neuronavigation and Augmentation System for Non-Invasive Brain Stimulation

TL;DR

This work addresses the need for accessible, precise neuronavigation in transcranial magnetic stimulation by introducing a low-cost optical-tag tracking system that uses multiple consumer cameras to track the TMS coil and patient head in real time. A Unity-based 3D brain model and AR overlays provide intuitive, in-situ visualization to guide coil placement, reducing reliance on abstract numerical displays. The approach combines AprilTag-based pose estimation, multi-camera fusion with Gaussian error combination, and a smartphone AR interface, achieving sub-millimeter depth precision and sub-degree orientation accuracy. The solution offers a practical path to broader adoption of TMS neuronavigation by lowering cost and complexity while maintaining competitive accuracy and usability.

Abstract

Accurate neuronavigation is critical for effective transcranial magnetic stimulation (TMS), as stimulation outcomes depend directly on precise coil placement. Existing neuronavigation systems are often costly, complex, and prone to tracking errors. To address these limitations, we present a computer vision based neuronavigation system that enables real time tracking of the patient and TMS instrumentation. The system integrates a multi camera optical tracking setup with consumer grade hardware and visible markers to drive a digital twin of the stimulation process. A dynamic 3D brain model in Unity updates in real time to visualize coil position and estimated stimulation targets. Augmented reality (AR) is further incorporated to project this model directly onto the patient's head, enabling intuitive, in situ coil adjustment without reliance on abstract numerical displays. Overall, the proposed approach improves spatial precision and accuracy while enhancing usability.
Paper Structure (16 sections, 6 equations, 6 figures)

This paper contains 16 sections, 6 equations, 6 figures.

Table of Contents

  1. Introduction
  2. Conventional Neuronavigation Systems
  3. Tag-Based Multi-Camera Tracking System
  4. Tracking Algorithm
  5. Multi-Camera Setup
  6. Optical Target Estimation
  7. Direct Linear Transform
  8. Pose Estimation
  9. Gaussian Error Combination for Increased Precision
  10. Experimental Setup
  11. Setup
  12. Augmented Reality Neuron Navigation System
  13. Results
  14. Precision and Accuracy
  15. Stimulation Point Localization Accuracy
  16. ...and 1 more sections

Figures (6)

  • Figure 1: (a) Components and (b) multi-camera TMS neuronavigation system during experiment. The system consists of a TMS coil, a multi-camera tracking setup for real-time spatial localization, and optical markers attached to the tracked objects and the head for position and orientation detection. The cameras provide synchronized tracking data and translate all components into a unified coordinate frame.
  • Figure 2: Visualization of the neuronavigation system. (a) Real-time 3D visualization of the TMS stimulation point integrated with a digital brain model in Unity. (b) Augmented reality interface displaying the stimulation point.
  • Figure 3: Probability distribution plots of the detected distance in the multi-camera neuronavigation system evaluation across five positions.
  • Figure 4: Probability distribution plots of the detected orientation in the multi-camera neuronavigation system evaluation across five positions.
  • Figure 5: Selected stimulation target localization accuracy.
  • ...and 1 more figures