Development of performance and learning rate evaluation models in robot-assisted surgery using electroencephalography and eye-tracking.

IF 3.6 1区 心理学 Q1 EDUCATION & EDUCATIONAL RESEARCH npj Science of Learning Pub Date : 2024-01-20 DOI:10.1038/s41539-024-00216-y
Somayeh B Shafiei, Saeed Shadpour, Farzan Sasangohar, James L Mohler, Kristopher Attwood, Zhe Jing
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Abstract

The existing performance evaluation methods in robot-assisted surgery (RAS) are mainly subjective, costly, and affected by shortcomings such as the inconsistency of results and dependency on the raters' opinions. The aim of this study was to develop models for an objective evaluation of performance and rate of learning RAS skills while practicing surgical simulator tasks. The electroencephalogram (EEG) and eye-tracking data were recorded from 26 subjects while performing Tubes, Suture Sponge, and Dots and Needles tasks. Performance scores were generated by the simulator program. The functional brain networks were extracted using EEG data and coherence analysis. Then these networks, along with community detection analysis, facilitated the extraction of average search information and average temporal flexibility features at 21 Brodmann areas (BA) and four band frequencies. Twelve eye-tracking features were extracted and used to develop linear random intercept models for performance evaluation and multivariate linear regression models for the evaluation of the learning rate. Results showed that subject-wise standardization of features improved the R2 of the models. Average pupil diameter and rate of saccade were associated with performance in the Tubes task (multivariate analysis; p-value = 0.01 and p-value = 0.04, respectively). Entropy of pupil diameter was associated with performance in Dots and Needles task (multivariate analysis; p-value = 0.01). Average temporal flexibility and search information in several BAs and band frequencies were associated with performance and rate of learning. The models may be used to objectify performance and learning rate evaluation in RAS once validated with a broader sample size and tasks.

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利用脑电图和眼动追踪技术开发机器人辅助手术的性能和学习率评估模型。
现有的机器人辅助手术(RAS)性能评估方法主要是主观性的,成本高,而且存在结果不一致和依赖于评分者意见等缺点。本研究的目的是开发一种模型,用于客观评估机器人辅助手术任务练习时的表现和机器人辅助手术技能的学习率。研究记录了 26 名受试者在完成插管、海绵缝合、点和针任务时的脑电图(EEG)和眼球追踪数据。表现评分由模拟器程序生成。利用脑电图数据和相干性分析提取大脑功能网络。然后,这些网络与群落检测分析相结合,有助于提取 21 个布罗德曼区(BA)和四个频带频率的平均搜索信息和平均时间灵活性特征。提取的 12 个眼动跟踪特征被用于建立线性随机截距模型以进行性能评估,以及多元线性回归模型以评估学习率。结果表明,对特征进行受试者标准化可提高模型的 R2。平均瞳孔直径和囊状移动速度与 "管子 "任务的成绩有关(多变量分析;p 值分别为 0.01 和 0.04)。瞳孔直径的熵与 "点 "和 "针 "任务的成绩有关(多变量分析;p 值 = 0.01)。多个 BA 和频带频率的平均时间灵活性和搜索信息与学习成绩和学习率有关。这些模型一旦通过更广泛的样本量和任务验证后,可用于客观评价 RAS 的成绩和学习率。
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CiteScore
5.40
自引率
7.10%
发文量
29
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