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.
The study objective was classification of skill level based on the topographical features of the electroencephalogram(EEG) during the most complex Fundamentals of Laparoscopic Surgery(FLS) task. We developed a novel microstate-based Common Spatial Pattern (CSP) analysis with linear discriminant analysis(LDA) classification that was compared with topography-preserving convolutional neural network(CNN) based approach to distinguish experts versus novices based on EEG. Ten expert surgeons and thirteen 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. 32-channel EEG during task performance was used to analyze spatial patterns of brain activity in 8 expert surgeons (2 dropouts due to data quality) and 13 novice medical residents. Besides conventional CSP analysis, microstate analysis was applied for preprocessing before CSP analysis for improved classification using LDA with 10-fold cross-validation. Also, a topography-preserving 3D CNN model (ESNet) was applied that considered both spatial and temporal information for the classification. Here, 5-fold cross-validation was repeated 10 times, and the results of each iteration of the testing data set were evaluated using indices, Accuracy, F1 score, Mathews Correlation Coefficient (MCC), sensitivity, and Specificity. Microstate-based CSP analysis found that while novices had primarily the frontal cortex involved for a maximum of spatial pattern vectors, experts had the hotspot of the spatial pattern vectors over the frontal and parietal cortices where the discriminating parietal brain region was supported by the Gradient-weighted Class Activation Mapping (Grad-CAM) of our 3D CNN-based model. Here, LDA with 10-fold cross-validation achieved more than 90% classification accuracy with microstate-based CSP, while conventional regularized CSP could reach around 80% classification accuracy. Then, 3D CNN provided the highest sensitivity of 99.30%, the highest specificity of 99.70%, the highest F1 score of 98.51%, and the highest MCC of 97.56%. Microstate-based CSP analysis improved the LDA classification (~90%) of experts versus novices based on EEG topography during a complex FLS task; however, combining the spatial and temporal information in the EEG topography preserving 3D CNN model significantly improved the classifier accuracy (>98%) in addition to providing mechanistic insights based on Grad-CAM analysis.
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