## 3 Results
In other words, the same EEG epoch cannot be present simultaneously in two different folds, while two different EEG epochs segmented from the same patient can appear in different folds and therefore, at a particular 10-fold validation run, one epoch could appear in the training set and the other in the test set. For each test battery, we compared the performance of different algorithms provided by PyCaret. PyCaret includes a variety of classification algorithms that can be used for machine learning tasks. These algorithms cover a wide range of classification techniques, from traditional algorithms such as, for instance, logistic regression and decision trees, to ensemble methods as for instance random forest and gradient boosting. PyCaret provides a unified interface to work with these algorithms, making it easy to compare and evaluate their performance on different datasets. To streamline the discussion, we focused on the performance of the two top-performing models for each battery, namely, AdaBoost and Decision Tree (the most performant models for Battery [B1]), and Random Forest and Light Gradient Boosting Machine (the top performers for the validation approaches described in [B2] and [B3]). To assess the effectiveness of the algorithms, we examined various evaluation metrics, including accuracy and AUC. The results demonstrated notable variations in performance across the different batteries and algorithms. Accuracy and AUC (Area Under the Curve) with a One vs. Rest (OvR) strategy [25] were considered for comparison. Accuracy measures the number of correct prediction made by the algorithm. Denoting with \(TCNT\), \(TEPI\) and \(TPNES\) and \(FCNT\), \(FEPI\) and \(FPNES\), the number of CNT, EPI and PNES subjects well or misclassified by the model, respectively, the model accuracy is:  
\[Accuracy=\frac{TCNT+TEPI+TPNES}{TCNT+TEPI+TPNES+FCNT+FEPI+FPNES}\]  
AUC was chosen as measure to assess the model's capability of distinguishing among classes. In the multiclass problem formulation, using the One-vs.-Rest (OvR) strategy, the evaluation of the AUC measure was performed individually for each class. Table 1 presents Accuracy and micro-average Area Under the Curve (AUC) of the classifier models built from the 215 \(\times\) 116 EEG dataset, where the 116 features described in Section 2 were extracted without epoching the EEG recordings. A 10-fold cross validation was exploited for perfomance evaluation (see the validation approach described in [B1]). Best values are highlighted in bold. The results show that also the Ada Boost model, which was the most-performing, reached low accuracy levels. Table 2 presents Accuracy and micro-average Area Under the Curve (AUC) of the classifiers trained on 22,261 \(\times\) 116 EEG dataset. A patient-aware 10-fold cross validation was exploited for perfomance evaluation (see the validation approach in [2]). Best values are highlighted in bold. The results obtained in terms of accuracy and AUC summarized in Table 2 show that, especially with regard to the best performing classifiers, i.e., Random Forest and AdaBoost, the EEG epoching combined with a patient-aware 10-fold cross validation strategy, did a slight improvement of performance. Table 3 summarizes Accuracy and micro-average Area Under the Curve (AUC) of the classifiers trained on 22,261 \(\times\) 116 EEG dataset. A 10-fold cross validation was exploited for perfomance evaluation (see the approach described in [B3]). Best values are highlighted in bold. The results obtained in terms of accuracy and AUC show a significant increase in performance with respect to the previous approaches. Figure 7 compares the approaches in [2] and [B3] in terms of learning curves for Light Gradient Boosting. Learning curves, in the context of machine learning, are graphical representations that depict the relationship between a model's performance and the amount of training data used. They provide insights into how the model's performance improves or stabilizes as more data become available for training.