Neural insights on expert surgeons’ mental workload during live robotic surgeries

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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Publication Details

Journal
Scientific Reports
Published
2025-04-09
DOI
https://doi.org/10.1038/s41598-025-96064-w
Citations
4
Primary Topic
Surgical Simulation and Training
Type
article
Field-Weighted Citation Impact
8.03

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Neural insights on expert surgeons’ mental workload during live robotic surgeries

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4 citations
Scientific Reports
Surgical Simulation and Training
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article

Neural insights on expert surgeons’ mental workload during live robotic surgeries

Marian Obuseh, Denny Yu, James Steward, Chandru P. Sundaram, Chiho Lim, Jackie Cha
article en
4 citations

Abstract

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.

Scientific ReportsVol. 15(1)
Purdue University West Lafayette (US), Indiana University Bloomington (US), Comprehensive Urology (US), Clemson University (US)
Purdue University, Intuitive Surgical, Purdue University Center for Cancer Research
Openalex Percentile: Top 3%
Surgical Simulation and Training
8.03
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