Distinguishing Laparoscopic Surgery Experts from Novices Using EEG Topographic Features

Takahiro Manabe; F N U Rahul; Yaoyu Fu; Xavier Intes; Steven D Schwaitzberg; Suvranu De; Lora Cavuoto; Anirban Dutta

doi:10.3390/brainsci13121706

Distinguishing Laparoscopic Surgery Experts from Novices Using EEG Topographic Features

Brain Sci. 2023 Dec 11;13(12):1706. doi: 10.3390/brainsci13121706.

Authors

Takahiro Manabe¹, F N U Rahul², Yaoyu Fu³, Xavier Intes^{2

4}, Steven D Schwaitzberg⁵, Suvranu De⁶, Lora Cavuoto³, Anirban Dutta¹

Affiliations

¹ School of Engineering, University of Lincoln, Lincoln LN6 7TS, UK.
² Centre for Modeling, Simulation, and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, MI 12180, USA.
³ Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY 14260, USA.
⁴ Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, MI 12180, USA.
⁵ School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, USA.
⁶ College of Engineering, Florida A&M University-Florida State University, Tallahassee, FL 32310, USA.

Abstract

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.

Keywords: common spatial pattern; deep neural networks; electroencephalogram; fundamentals of laparoscopic surgery; skill classification; temporal–spatial pattern recognition.

Grants and funding

The authors appreciate the support of this study through the Medical Technology Enterprise Consortium (MTEC) award, #W81XWH2090019 (2020-628), and the US Army Futures Command, Combat Capabilities Development Command Soldier Centre STTC cooperative research agreement, #W912CG-21-2-0001. T.M. was funded by the pump priming grant from the school of engineering, University of Lincoln, UK for the writing—review and editing.