Lim Chiho, Obuseh Marian, Cha Jackie, Steward James, Sundaram Chandru, Yu Denny
Edwardson School of Industrial Engineering, Purdue University, West Lafayette, USA.
Department of Industrial Engineering, Clemson University, Clemson, USA.
Sci Rep. 2025 Apr 9;15(1):12073. doi: 10.1038/s41598-025-96064-w.
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.
尽管机器人手术已被采用并具有诸多益处，但它可能会给外科医生带来额外的心理负担。大多在手术结束时进行的经过验证的问卷调查可能无法准确反映整个手术过程中心理负担的动态变化。因此，我们试图确定基于脑电图（EEG）的不同脑区神经活动是否能够在术中测量专家外科医生心理负担的变化。我们收集了五位不同外科医生进行13台机器人辅助泌尿外科手术时的EEG数据。数据分析聚焦于三个手术阶段（术前、关键期和术后）。在每个阶段结束后，外科医生对他们感知到的心理负担进行评分。应用线性混合效应模型来探究研究阶段对EEG信号相对频谱带功率的影响。在手术的关键部分，额叶脑区的相对θ波带功率最高（p < 0.05）。随着主观评分的增加，相对额叶θ波带功率增加（p < 0.001），而在所有阶段中，相对顶叶α波带功率均降低。我们表明，EEG信号能够区分机器人手术中的术中负担。这具有多种应用，包括预测病例复杂性增加的风险因素以及外科手术教育。
