BRIEF RESEARCH REPORT article

Front. Hum. Neurosci.

Sec. Brain-Computer Interfaces

Volume 19 - 2025 | doi: 10.3389/fnhum.2025.1611524

This article is part of the Research TopicBrain-Computer Interfaces (BCIs) for daily activities: Innovations in EEG signal analysis and machine learning approachesView all articles

Predicting Task Performance in Robot-Assisted Surgery Using Physiological Stress and Subjective Workload: A Case Study with Interpretable Machine Learning

Provisionally accepted
Kaiqi WeiKaiqi Wei1Chika KimuraChika Kimura1Megumi ShimuraMegumi Shimura1Yoshihiro ShimomuraYoshihiro Shimomura2*Xue ZhaoXue Zhao3Takaaki TamuraTakaaki Tamura3Shinichi SakamotoShinichi Sakamoto3
  • 1Department of Design, Division of Creative Engineering, Graduate School of Science and Engineering, Chiba University, Chiba, Japan
  • 2Design Research Institute, Chiba University, Chiba, Japan
  • 3Department of Urology, Graduate School of Medicine, Chiba University, Chiba, Japan

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Robot-assisted surgery (RAS) enhances surgical precision and extends surgeons' capabilities. However, its effects on the cognitive and physical states of surgeons remain poorly understood. It is essential to investigate the workload and physiological stress surgeons experience during RAS. This case study employs a neuroergonomic approach to explore how these factors relate to task performance. A single expert surgeon performed simulated surgical tasks under systematically varied conditions (noise level, surgical posture and task type) to elicit variations in stress and workload. During the tasks, multiple physiological signals were recorded, including electroencephalography (EEG), electromyography (EMG), heart rate (HR), and electrodermal activity (EDA). Subjective workload was also assessed using the NASA-TLX and SURG-TLX.Several classification models, including CatBoost, random forest, logistic regression, and support vector machines, were trained to predict task performance. Among them, CatBoost demonstrated the highest predictive accuracy (79.5%) and achieved an area under the curve (AUC) of 0.807. The model interpretation was conducted using SHapley Additive exPlanations (SHAP). The analysis revealed that subjective workload, mean HR, and muscle activation were the most influential predictors. EEG-related features contributed variably across conditions. This study shows that integrating subjective assessments with physiological measures can effectively predict surgical task performance under stress.

Keywords: robot-assisted surgery (RAS), neuroergonomics, Physiological stress, Surgeon workload, machine learning, SHAP (SHapley Additive exPlanations), Task performance prediction

Received: 14 Apr 2025; Accepted: 27 May 2025.

Copyright: © 2025 Wei, Kimura, Shimura, Shimomura, Zhao, Tamura and Sakamoto. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Yoshihiro Shimomura, Design Research Institute, Chiba University, Chiba, Japan

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