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An Integrated Electroencephalography and Eye-Tracking Analysis Using eXtreme Gradient Boosting for Mental Workload

Somayeh B Shafiei1, Saeed Shadpour2, James L Mohler1

  • 1Roswell Park Comprehensive Cancer Center, USA.

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Summary

Advanced machine learning models predict surgical mental workload using electroencephalogram (EEG) and eye-tracking data. This approach enhances surgical training and task design by accurately assessing cognitive demands.

Keywords:
functional brain networkfundamentals of laparoscopic surgerynetwork communityrobot-assisted surgerysurgical training

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Area of Science:

  • Neuroscience and Biomedical Engineering
  • Cognitive Science in Medicine
  • Surgical Education Technology

Background:

  • Traditional mental workload assessment relies on subjective self-report scales, introducing bias.
  • Mental workload is complex and varies across surgical tasks, necessitating objective evaluation methods.
  • Identifying key contributing factors to mental workload is crucial for optimizing surgical procedures and training.

Purpose of the Study:

  • To develop and validate machine learning models for predicting mental workload during surgical tasks.
  • To integrate electroencephalogram (EEG) and eye-tracking data for enhanced workload prediction.
  • To identify critical features influencing mental workload in surgical simulations.

Main Methods:

  • Utilized EEG and eye-tracking data from 26 participants performing simulated surgical tasks (da Vinci simulator, FLS program).
  • Developed an eXtreme Gradient Boosting (XGBoost) model for mental workload evaluation.
  • Analyzed features including pupil diameter, task complexity, temporal lobe functional connectivity, and eye movement trajectories.

Main Results:

  • XGBoost models achieved high predictive performance (R²: 0.81-0.83) across simulated surgical tasks.
  • Key predictors included pupil diameter, task complexity, temporal lobe connectivity, and eye movement patterns.
  • Integrating EEG and eye-tracking data significantly improved model performance (p < 0.05), except for the Pattern Cut task.

Conclusions:

  • Machine learning models integrating multimodal data show strong potential for objective mental workload prediction in surgery.
  • This approach can inform surgical task design and personalize surgical training programs.
  • Further research may refine models for specific tasks and explore additional neurophysiological correlates of cognitive load.