Brain Sciences (Dec 2023)

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
https://doi.org/10.3390/brainsci13121706
Journal volume & issue
Vol. 13, no. 12
p. 1706

Abstract

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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.

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