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

• Published in: Human factors Vol. 67; no. 5; pp. 464 - 484
• Main Authors: Shafiei, Somayeh B., Shadpour, Saeed, Mohler, James L.
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.05.2025; Human Factors and Ergonomics Society
• Subjects: Adult; EEG; Electroencephalography; Electroencephalography - methods; Eye movements; Eye-Tracking Technology; Female; Humans; Laparoscopy; Learning algorithms; Machine Learning; Male; Medical education; Performance enhancement; Performance evaluation; Prediction models; Psychomotor Performance - physiology; Robotic surgery; Surgery; Sutures; Task Performance and Analysis; Temporal lobe; Tracking; Workload; Workloads; Young Adult; functional brain network; surgical training; robot-assisted surgery; network community; fundamentals of laparoscopic surgery
• ISSN: 0018-7208; 1547-8181; 1547-8181
• DOI: 10.1177/00187208241285513

Objective We aimed to develop advanced machine learning models using electroencephalogram (EEG) and eye-tracking data to predict the mental workload associated with engaging in various surgical tasks. Background Traditional methods of evaluating mental workload often involve self-report scales, which are subject to individual biases. Due to the multidimensional nature of mental workload, there is a pressing need to identify factors that contribute to mental workload across different surgical tasks. Method EEG and eye-tracking data from 26 participants performing Matchboard and Ring Walk tasks from the da Vinci simulator and the pattern cut and suturing tasks from the Fundamentals of Laparoscopic Surgery (FLS) program were used to develop an eXtreme Gradient Boosting (XGBoost) model for mental workload evaluation. Results The developed XGBoost models demonstrated strong predictive performance with R2 values of 0.82, 0.81, 0.82, and 0.83 for the Matchboard, Ring Walk, pattern cut, and suturing tasks, respectively. Key features for predicting mental workload included task average pupil diameter, complexity level, average functional connectivity strength at the temporal lobe, and the total trajectory length of the nondominant eye’s pupil. Integrating features from both EEG and eye-tracking data significantly enhanced the performance of mental workload evaluation models, as evidenced by repeated-measures t-tests yielding p-values less than 0.05. However, this enhancement was not observed in the Pattern Cut task (repeated-measures t-tests; p > 0.05). Conclusion The findings underscore the potential for machine learning and multidimensional feature integration to predict mental workload and thereby improve task design and surgical training. Application The advanced mental workload prediction models could serve as instrumental tools to enhance our understanding of surgeons’ cognitive demands and significantly improve the effectiveness of surgical training programs.