EEG-Enhanced Adaptive Training: Assessing Cognitive Load and Stress in Surgical Training Simulations
This project explores the integration of the IDUN Guardian EEG sensor into an AR surgical simulator to adaptively assess and respond to trainees' stress and cognitive loads, aiming to personalize training and enhance skill acquisition. By analyzing EEG data with advanced signal processing and machine learning, the project seeks to improve surgical training outcomes and standardize education across the field. Background Surgeries consist of a series of complex tasks and external factors such as unexpected complications or changes in the patient's condition can impact the course of the surgery. The digitization of surgical training can reduce the risk of surgical errors, improve patient outcomes, and provide standardized training and assessment. This transformation allows for safer and more efficient training of surgeons, ensuring that the surgical staff receives consistent education allowing for easier comparison of skills across different individuals and institutions. At the ROCS group, located at Balgrist University Hospital in Zurich, we are developing a surgical training simulator for adaptive Augmented Reality-based training for orthopedic surgeries. An automated assessment of stress and cognitive load of the trainee can play an important role in automated assessment and adaptive, personalized surgical training. In this context, we want to explore the capabilities of the IDUN guardian, an in-ear EEG sensor developed by an ETH Spin-off (https://iduntechnologies.com/idun-guardian-in-ear-eeg-platform/).
Investigating NeRF and Gaussian Splatting for 3D Lumbar Vertebrae Reconstruction from Intraoperative X-Rays
This master's thesis delves into the surgical application of Neural Radiance Fields (NeRF) and Gaussian splatting to enhance 3D reconstruction of lumbar vertebrae using intraoperative X-rays. The study focuses on understanding NeRF fundamentals, comparing NeRF-based approaches with traditional methods, and optimizing NeRF for precise 3D reconstruction. The expected outcomes comprise a comprehensive grasp of NeRF and insights into its potential for surgical applications. The findings are intended to benefit orthopedic surgical planning, contributing to improved surgical outcomes

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