Manabe Takahiro, Rahul F N U, Fu Yaoyu, Intes Xavier, Schwaitzberg Steven D, De Suvranu, Cavuoto Lora, Dutta Anirban
School of Engineering, University of Lincoln, Lincoln LN6 7TS, UK.
Centre for Modeling, Simulation, and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, MI 12180, USA.
Brain Sci. 2023 Dec 11;13(12):1706. doi: 10.3390/brainsci13121706.
The study aimed to differentiate experts from novices in laparoscopic surgery tasks using electroencephalogram (EEG) topographic features. A microstate-based common spatial pattern (CSP) analysis with linear discriminant analysis (LDA) was compared to a topography-preserving convolutional neural network (CNN) approach. Expert surgeons (N = 10) and novice medical residents (N = 13) performed laparoscopic suturing tasks, and EEG data from 8 experts and 13 novices were analysed. Microstate-based CSP with LDA revealed distinct spatial patterns in the frontal and parietal cortices for experts, while novices showed frontal cortex involvement. The 3D CNN model (ESNet) demonstrated a superior classification performance (accuracy > 98%, sensitivity 99.30%, specificity 99.70%, F1 score 98.51%, MCC 97.56%) compared to the microstate based CSP analysis with LDA (accuracy ~90%). Combining spatial and temporal information in the 3D CNN model enhanced classifier accuracy and highlighted the importance of the parietal-temporal-occipital association region in differentiating experts and novices.
该研究旨在利用脑电图（EEG）地形特征区分腹腔镜手术任务中的专家和新手。将基于微状态的共同空间模式（CSP）分析与线性判别分析（LDA）与一种保留地形的卷积神经网络（CNN）方法进行了比较。专家外科医生（N = 10）和新手住院医生（N = 13）进行了腹腔镜缝合任务，并对8名专家和13名新手的EEG数据进行了分析。基于微状态的CSP与LDA显示，专家在额叶和顶叶皮质有明显的空间模式，而新手则表现为额叶皮质参与。与基于微状态的CSP分析与LDA（准确率约90%）相比，3D CNN模型（ESNet）表现出卓越的分类性能（准确率> 98%，灵敏度99.30%，特异性99.70%，F1分数98.51%，MCC 97.56%）。在3D CNN模型中结合空间和时间信息提高了分类器的准确率，并突出了顶叶 - 颞叶 - 枕叶联合区域在区分专家和新手方面的重要性。
