Objective: The objective of this study was to evaluate the differences in brain activity between expert surgeons and novice medical residents based on electroencephalography (EEG). The first sub-goal was to assess the Microstate EEGlab toolbox and BCIlab toolboxes for data analysis and classification of the topographical features for microstate-based Common Spatial Pattern (CSP) analysis. Then, the second sub-goal was to compare microstate-based CSP with the conventional regularized CSP approach.
Methods: After IRB approval, ten expert surgeons and 13 novice medical residents were recruited at the University at Buffalo. After informed consent, the subjects performed three trials of laparoscopic suturing and knot tying with rest periods in-between the task trials. 32-channel EEG was performed during the task performance that was used to analyze spatial patterns of brain activity in 8 expert surgeons (2 dropouts due to data quality) and 13 novice medical residents. Microstate analysis was applied as preprocessing to improve the signal-to-noise ratio before CSP analysis, distinguishing expert surgeons' brain activity from novice medical residents.
Results: Microstate-based CSP analysis identified the significant channels based on the maximum spatial pattern vectors at the scalp. While novices had primarily the frontal cortex involved for a maximum of the spatial pattern vectors at the scalp, the experts had the hotspot of the spatial pattern vectors over the frontal and parietal cortices. Simple linear discriminant analysis with 10-fold cross-validation achieved more than 90% classification accuracy with microstate-based CSP, while the conventional regularized CSP could reach around 80% classification accuracy.
Conclusion and Discussion: Microstate-based CSP analysis can identify an optimal set of channels for evaluating the differences in brain activity between expert surgeons and novice medical residents. Future studies can apply microstate-based monitoring of the temporal dynamics of the brain behavior for an individualized adaptive VR-based training paradigm.