Objective: In this study, we evaluated differences in the brain activity between expert surgeons and novice medical residents based on electroencephalography (EEG). We used the EEGlab toolbox for data analysis and classification using Common spatial pattern (CSP) analysis.

Methods: 10 expert surgeons and 10 novice medical residents were recruited at the University at Buffalo after IRB approval. 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. CSP analysis was used to distinguish the brain activity of expert surgeons from novice medical residents.

Results: CSP analysis identified the significant channels based on the maximum of the spatial pattern vectors at the scalp. While novices had primarily 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 frontal and parietal cortices. Simple linear discriminant analysis with 10-fold cross-validation achieved more than 90% classification accuracy using the spatial pattern vectors at the scalp.

Conclusion: CSP analysis can identify an optimal set of channels for evaluating the differences in brain activity between expert surgeons and novice medical residents. CSP analysis also provided the features for a simple linear discriminant analysis to classify expert surgeons versus novice medical residents using an optimal set of channels overlying frontal and parietal cortices.

Anirban Dutta

Affiliation

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EEG features for assessing skill levels during laparoscopic surgical training