This literature, presented by Hu et al., evaluates the accuracy of a machine learning (ML) model that predicts the surgical outcome of patients with drug-resistant Temporal Lobe Epilepsy (TLE). The ML model was constructed using a support vector machine (SVM) that utilized a dataset containing cortico-cortical evoked potentials (CCEPs), the surgical resection area, and connectivity comparisons between inside and outside the resection area obtained from intracranial electroencephalography (iEEG). The model was applied to 56 1-year postoperative TLE patients who underwent stereo-electroencephalography electrode implantation (with a Perisylvian pattern) and single-pulse electrical stimulation (SPES) tests to evaluate the prediction model accuracy. Using a linear SVM model, the accuracy, sensitivity, specificity, and F1-score output values were 0.800, 0.750, 0.857, and 0.800, respectively. SHAP analysis was performed, confirming that the connection strength within and outside the resection area has a strong influence on surgical prognosis. By assessing connectivity using N1_out (early pyramidal neuronal activation) and N2_out (late, long-lasting inhibitory response) iEEG values, they found that the non-seizure-free group exhibited both higher N1_out and N2_out values compared to the seizure-free group. These values were the two most important prediction features and demonstrated a positive correlation with postoperative epilepsy recurrence. This study corroborates the accuracy of models based on connectivity strength between resection areas in predicting surgical outcomes for patients with TLE and SEEG implantation.