Why the study?
Post-procedure validated questionnaires may not accurately capture dynamic intraoperative mental workload during robotic surgeries, motivating whether electroencephalogram-based neural activities can measure these variations.
Can EEG-based neural activity monitoring measure variations in expert surgeons' mental workload intraoperatively during robotic surgeries?
Can EEG-based neural activity monitoring measure variations in expert surgeons' mental workload intraoperatively during robotic surgeries?
EEG signals can effectively distinguish intraoperative mental workload in expert surgeons during robotic surgeries, offering potential applications in surgical education and predicting case complexity.
EEG may track dynamic mental workload in robotic surgery; leaves open clinical utility and need for prospective validation.
Despite its adoption and benefits, robotic surgeries can impose additional mental workload on surgeons. Validated questionnaires mostly administered at the end of procedures may not accurately capture the dynamic nature of mental workload over an entire procedure. Hence, we sought to determine if electroencephalogram (EEG) based neural activities in different brain regions can measure variations in expert surgeons' mental workload intraoperatively. EEG data was collected from five different surgeons performing 13 robotic-assisted urological procedures. Data analysis focused on three surgery phases (before, critical, and after). After performing each phase, surgeons provided a rating of their perceived mental workload. A linear mixed effects model was applied to explore the impact of the study phases on the relative spectral band power of EEG signals. The relative theta band power in the frontal brain region was highest during the critical portions of the procedure (p < 0.05). As the subjective ratings increased, the relative frontal theta band power increased (p < 0.001) while the relative parietal alpha band power decreased across all phases. We show that EEG signals can distinguish intraoperative workload in robotic surgeries. This has several applications including predicting risk factors for increased case complexity and surgical education.
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Lim et al. (2025) studied this question.
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