## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Feature Set** & **Classifier** & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **AUC** \\ \hline \multirow{3}{*}{Statistical} & RF & \(78.45\pm 0.85\) & \(78.9\pm 1.02\) & \(78.45\pm 0.85\) & \(78.49\pm 0.86\) & \(0.8563\pm 0.0056\) \\ \cline{2-7}  & KNN & \(76.76\pm 1.46\) & \(76.78\pm 1.44\) & \(76.76\pm 1.46\) & \(76.67\pm 1.47\) & \(0.8451\pm 0.0097\) \\ \cline{2-7}  & MLP & \(76.14\pm 1.22\) & \(76.37\pm 1.09\) & \(76.14\pm 1.22\) & \(76.06\pm 1.23\) & \(0.8409\pm 0.0081\) \\ \hline \multirow{3}{*}{FD} & RF & \(76.78\pm 0.98\) & \(77.39\pm 0.9\) & \(76.78\pm 0.98\) & \(76.85\pm 0.98\) & \(0.8452\pm 0.0065\) \\ \cline{2-7}  & KNN & \(76.70\pm 1.08\) & \(76.68\pm 1.08\) & \(76.70\pm 1.08\) & \(76.59\pm 1.06\) & \(0.8446\pm 0.0072\) \\ \cline{2-7}  & MLP & \(75.41\pm 0.44\) & \(75.47\pm 0.41\) & \(75.40\pm 0.44\) & \(75.33\pm 0.45\) & \(0.836\pm 0.0029\) \\ \hline \multirow{3}{*}{HOS} & RF & \(67.75\pm 1.64\) & \(68.47\pm 1.48\) & \(67.75\pm 1.64\) & \(67.82\pm 1.65\) & \(0.7850\pm 0.0109\) \\ \cline{2-7}  & KNN & \(59.40\pm 0.5\) & \(59.43\pm 0.55\) & \(59.40\pm 0.51\) & \(59.17\pm 0.47\) & \(0.7294\pm 0.0034\) \\ \cline{2-7}  & MLP & \(65.29\pm 1.29\) & \(65.53\pm 1.54\) & \(65.29\pm 1.29\) & \(65.15\pm 1.29\) & \(0.7686\pm 0.0086\) \\ \hline \multirow{3}{*}{Entropy} & RF & \(65.50\pm 0.91\) & \(66.46\pm 0.86\) & \(65.50\pm 0.91\) & \(65.62\pm 0.93\) & \(0.7700\pm 0.0061\) \\ \cline{2-7}  & KNN & \(60.88\pm 1.75\) & \(61.25\pm 1.82\) & \(60.88\pm 1.75\) & \(60.59\pm 1.75\) & \(0.7392\pm 0.0116\) \\ \cline{2-7}  & MLP & \(65.44\pm 0.84\) & \(65.52\pm 0.77\) & \(65.45\pm 0.84\) & \(65.37\pm 0.86\) & \(0.7696\pm 0.00560\) \\ \hline \multirow{3}{*}{Combined} & RF & \(76.75\pm 1.11\) & \(77.41\pm 1.17\) & \(76.75\pm 1.11\) & \(76.82\pm 1.14\) & \(0.8450\pm 0.7402\) \\ \cline{2-7}  & KNN & \(59.40\pm 0.5\) & \(59.43\pm 0.55\) & \(59.40\pm 0.51\) & \(59.17\pm 0.47\) & \(0.7294\pm 0.0034\) \\ \cline{1-1} \cline{2-7}  & MLP & \(76.19\pm 0.5\) & \(76.30\pm 0.49\) & \(76.19\pm 0.50\) & \(76.15\pm 0.55\) & \(0.8413\pm 0.00342\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average classification performance of classroom EEG recordings based on statistical, FD, HOS, entropy, and combined features of EEG. All ACC, PRE, REC, and F1-S are given as % \(\pm\) SD. The performance highlighted in pink color represents the highest performance scores across the feature sets. are influenced by the specific context and nature of the activity. Each person's unique cognitive and emotional response to different tasks may lead to variations in how brain activity evolves over time. Figure 5: Comparison of classification performance with various features (in terms of accuracy). Figure 5 shows the classification performance, reflecting the performance of the statistical features using RF, KNN, and MLP classifiers, illustrating the time-domain statistical characteristics of EEG signals that can effectively discriminate reading, discussion, lecture, and video learning-style patterns. The confusion matrix, which is illustrated in Figure 6a, provided further insights into the performance results in accurately categorizing instances. Significantly, accurate classifications were made for 341 occurrences of discussion, 384 occurrences of lecture, 347 occurrences of reading, and 375 occurrences of video. Nevertheless, the model demonstrated its shortcomings through the misclassification of instances in diverse contexts. For example, 72 occurrences of discussion were incorrectly classified as lecture, which suggests areas that could be enhanced. Figure 6: Confusion matrix of a fold using statistical features obtained from (**a**) RF, (**b**) KNN, and (**c**) MLP. The diagonal elements are the correctly recognized samples. From the \(p\)-values displayed in Figure 8, it is evident that all pair-wise comparisons between the learning methods--reading, video, lecture, and discussion--show statistically significant differences.