Toward Personalized Surgeons' Cognitive Workload: A Linear Mixed-Effects Model Analysis of EEG Features

Surgical Flow Disruptions in the operating room can increase cognitive workload and the risk of technical errors. While electroencephalography (EEG) provides an objective framework for monitoring mental effort, traditional statistical methods often rely on group-level analysis. This approach potentially ignores the significant degree of inter-subject variability among individuals. This study evaluated the effectiveness of various EEG features in monitoring the impact of SFDs using a Linear Mixed-Effects Model (LMEM) framework to distinguish between group trends and individual-specific responses. EEG data were recorded from nine surgical trainees during a simulated microvascular anastomosis, during which participants were subjected to six predefined SFDs, including auditory alerts and visual distractions. EEG features were analyzed using LMEM with random intercepts and slopes, and a subjectivity index was introduced to quantify the degree of individualization across 36 extracted EEG features. An initial analysis of variance on the fixed effects revealed that, generally, theta and beta power bands were significantly impacted by disruptions across the group. However, the subjectivity index identified several features, such as Gamma Asymmetry, that exhibited high individualization despite the absence of a significant group trend. Features characterized by both significant fixed effects and high subjectivity indices, such as Mean Power Beta, were identified as the most reliable metrics for tracking cognitive load. These results demonstrate that traditional group-averaging methods can mask critical individual physiological responses. By utilizing LMEM and the associated subjectivity index, this research establishes a statistical foundation for developing personalized intraoperative monitoring systems tailored to a surgeon's unique cognitive profile.