fig5

A multimodal approach combining tool-pressure and EEG features for laparoscopic skill classification using machine learning

Figure 5. Classification accuracies across four EEG frequency bands using three classifiers: RFC, GPC, and ABC. (A) Delta band; (B) Theta band; (C) Alpha band; and (D) Beta band. The y-axis represents classification accuracy, and the x-axis indicates the classifier type for each frequency band. Three feature sets were compared: asymmetry-only (red), asymmetry combined with EEG PSD (light gray), and asymmetry combined with EEG PLV (dark gray). Accuracy values were obtained using cross-validation procedures as described in the Methods section. EEG: Electroencephalography; RFC: random forest classifier; GPC: Gaussian process classifier; ABC: AdaBoost classifier; PSD: power spectral density; PLV: phase-locking value.

Artificial Intelligence Surgery
ISSN 2771-0408 (Online)
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