{"title":"利用极梯度提升技术对脑电图和眼动跟踪进行综合分析，以评估外科手术中的脑力劳动负荷。","authors":"Somayeh B Shafiei, Saeed Shadpour, James L Mohler","doi":"10.1177/00187208241285513","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>We aimed to develop advanced machine learning models using electroencephalogram (EEG) and eye-tracking data to predict the mental workload associated with engaging in various surgical tasks.</p><p><strong>Background: </strong>Traditional methods of evaluating mental workload often involve self-report scales, which are subject to individual biases. Due to the multidimensional nature of mental workload, there is a pressing need to identify factors that contribute to mental workload across different surgical tasks.</p><p><strong>Method: </strong>EEG and eye-tracking data from 26 participants performing Matchboard and Ring Walk tasks from the da Vinci simulator and the pattern cut and suturing tasks from the Fundamentals of Laparoscopic Surgery (FLS) program were used to develop an eXtreme Gradient Boosting (XGBoost) model for mental workload evaluation.</p><p><strong>Results: </strong>The developed XGBoost models demonstrated strong predictive performance with <i>R</i><sup>2</sup> values of 0.82, 0.81, 0.82, and 0.83 for the Matchboard, Ring Walk, pattern cut, and suturing tasks, respectively. Key features for predicting mental workload included task average pupil diameter, complexity level, average functional connectivity strength at the temporal lobe, and the total trajectory length of the nondominant eye's pupil. Integrating features from both EEG and eye-tracking data significantly enhanced the performance of mental workload evaluation models, as evidenced by repeated-measures t-tests yielding <i>p</i>-values less than 0.05. However, this enhancement was not observed in the Pattern Cut task (repeated-measures t-tests; <i>p</i> > 0.05).</p><p><strong>Conclusion: </strong>The findings underscore the potential for machine learning and multidimensional feature integration to predict mental workload and thereby improve task design and surgical training.</p><p><strong>Application: </strong>The advanced mental workload prediction models could serve as instrumental tools to enhance our understanding of surgeons' cognitive demands and significantly improve the effectiveness of surgical training programs.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Integrated Electroencephalography and Eye-Tracking Analysis Using eXtreme Gradient Boosting for Mental Workload Evaluation in Surgery.\",\"authors\":\"Somayeh B Shafiei, Saeed Shadpour, James L Mohler\",\"doi\":\"10.1177/00187208241285513\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>We aimed to develop advanced machine learning models using electroencephalogram (EEG) and eye-tracking data to predict the mental workload associated with engaging in various surgical tasks.</p><p><strong>Background: </strong>Traditional methods of evaluating mental workload often involve self-report scales, which are subject to individual biases. Due to the multidimensional nature of mental workload, there is a pressing need to identify factors that contribute to mental workload across different surgical tasks.</p><p><strong>Method: </strong>EEG and eye-tracking data from 26 participants performing Matchboard and Ring Walk tasks from the da Vinci simulator and the pattern cut and suturing tasks from the Fundamentals of Laparoscopic Surgery (FLS) program were used to develop an eXtreme Gradient Boosting (XGBoost) model for mental workload evaluation.</p><p><strong>Results: </strong>The developed XGBoost models demonstrated strong predictive performance with <i>R</i><sup>2</sup> values of 0.82, 0.81, 0.82, and 0.83 for the Matchboard, Ring Walk, pattern cut, and suturing tasks, respectively. Key features for predicting mental workload included task average pupil diameter, complexity level, average functional connectivity strength at the temporal lobe, and the total trajectory length of the nondominant eye's pupil. Integrating features from both EEG and eye-tracking data significantly enhanced the performance of mental workload evaluation models, as evidenced by repeated-measures t-tests yielding <i>p</i>-values less than 0.05. However, this enhancement was not observed in the Pattern Cut task (repeated-measures t-tests; <i>p</i> > 0.05).</p><p><strong>Conclusion: </strong>The findings underscore the potential for machine learning and multidimensional feature integration to predict mental workload and thereby improve task design and surgical training.</p><p><strong>Application: </strong>The advanced mental workload prediction models could serve as instrumental tools to enhance our understanding of surgeons' cognitive demands and significantly improve the effectiveness of surgical training programs.</p>\",\"PeriodicalId\":56333,\"journal\":{\"name\":\"Human Factors\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Human Factors\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://doi.org/10.1177/00187208241285513\",\"RegionNum\":3,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BEHAVIORAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human Factors","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1177/00187208241285513","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BEHAVIORAL SCIENCES","Score":null,"Total":0}
引用次数: 0
摘要
目的：我们旨在利用脑电图（EEG）和眼动跟踪数据开发先进的机器学习模型：我们旨在利用脑电图（EEG）和眼球跟踪数据开发先进的机器学习模型，以预测与从事各种外科手术任务相关的脑力劳动负荷：背景：传统的脑力劳动负荷评估方法通常采用自我报告量表，这种方法会受到个体偏差的影响。由于脑力劳动负荷具有多维性，因此迫切需要找出导致不同手术任务中脑力劳动负荷的因素：方法：利用26名参与者在执行达芬奇模拟器中的火柴盒和环形行走任务以及腹腔镜手术基础（FLS）课程中的图案切割和缝合任务时获得的脑电图和眼动追踪数据，开发了用于心理工作量评估的极梯度提升（XGBoost）模型：结果：所开发的 XGBoost 模型具有很强的预测性能，对 Matchboard、Ring Walk、图案切割和缝合任务的 R2 值分别为 0.82、0.81、0.82 和 0.83。预测心理工作量的关键特征包括任务的平均瞳孔直径、复杂程度、颞叶的平均功能连接强度以及非优势眼瞳孔的总轨迹长度。将脑电图和眼动跟踪数据的特征整合在一起，能显著提高心理工作量评估模型的性能，重复测量 t 检验的 p 值小于 0.05 即为证明。然而，在 "图案剪切 "任务中却没有观察到这种提高（重复测量 t 检验；p > 0.05）：研究结果强调了机器学习和多维特征整合在预测脑力劳动负荷方面的潜力，从而改进了任务设计和手术训练：先进的脑力劳动负荷预测模型可作为工具，增强我们对外科医生认知需求的了解，并显著提高外科培训计划的有效性。
An Integrated Electroencephalography and Eye-Tracking Analysis Using eXtreme Gradient Boosting for Mental Workload Evaluation in Surgery.
Objective: We aimed to develop advanced machine learning models using electroencephalogram (EEG) and eye-tracking data to predict the mental workload associated with engaging in various surgical tasks.
Background: Traditional methods of evaluating mental workload often involve self-report scales, which are subject to individual biases. Due to the multidimensional nature of mental workload, there is a pressing need to identify factors that contribute to mental workload across different surgical tasks.
Method: EEG and eye-tracking data from 26 participants performing Matchboard and Ring Walk tasks from the da Vinci simulator and the pattern cut and suturing tasks from the Fundamentals of Laparoscopic Surgery (FLS) program were used to develop an eXtreme Gradient Boosting (XGBoost) model for mental workload evaluation.
Results: The developed XGBoost models demonstrated strong predictive performance with R2 values of 0.82, 0.81, 0.82, and 0.83 for the Matchboard, Ring Walk, pattern cut, and suturing tasks, respectively. Key features for predicting mental workload included task average pupil diameter, complexity level, average functional connectivity strength at the temporal lobe, and the total trajectory length of the nondominant eye's pupil. Integrating features from both EEG and eye-tracking data significantly enhanced the performance of mental workload evaluation models, as evidenced by repeated-measures t-tests yielding p-values less than 0.05. However, this enhancement was not observed in the Pattern Cut task (repeated-measures t-tests; p > 0.05).
Conclusion: The findings underscore the potential for machine learning and multidimensional feature integration to predict mental workload and thereby improve task design and surgical training.
Application: The advanced mental workload prediction models could serve as instrumental tools to enhance our understanding of surgeons' cognitive demands and significantly improve the effectiveness of surgical training programs.
期刊介绍：
Human Factors: The Journal of the Human Factors and Ergonomics Society publishes peer-reviewed scientific studies in human factors/ergonomics that present theoretical and practical advances concerning the relationship between people and technologies, tools, environments, and systems. Papers published in Human Factors leverage fundamental knowledge of human capabilities and limitations – and the basic understanding of cognitive, physical, behavioral, physiological, social, developmental, affective, and motivational aspects of human performance – to yield design principles; enhance training, selection, and communication; and ultimately improve human-system interfaces and sociotechnical systems that lead to safer and more effective outcomes.
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