## 4 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature Selection** & **Algorithm Name** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** \\ \hline \multirow{6}{*}{Support Vector Machine-based Recursive Feature Elimination} & SVM & 93.54 & 94.68 & 95.97 & 95.29 \\  & Logistic Regression & 92.86 & 93.46 & 96.46 & 94.84 \\  & XGBoost & 77.55 & 80.99 & 87.44 & 84.07 \\  & LGBM & 75.18 & 80.01 & 84.94 & 82.3 \\  & Random Forest & 75.17 & 78.56 & 87.42 & 82.67 \\  & CatBoost & 74.15 & 77.89 & 86.41 & 81.89 \\ \hline \multirow{6}{*}{Elastic Net} & SVM & 90.47 & 92.91 & 93.44 & 93.01 \\  & Logistic Regression & 90.47 & 92.71 & 93.44 & 93.0 \\  & Random Forest & 77.87 & 80.19 & 89.4 & 84.43 \\  & XGBoost & 77.56 & 82.85 & 84.44 & 83.57 \\  & CatBoost & 76.2 & 80.63 & 85.44 & 82.94 \\  & LGBM & 75.51 & 80.13 & 84.92 & 82.45 \\ \hline \multirow{6}{*}{Mutual Information} & Random Forest & 77.53 & 82.08 & 85.37 & 83.62 \\  & LGBM & 76.19 & 81.33 & 84.4 & 82.69 \\  & CatBoost & 75.16 & 79.45 & 85.4 & 82.21 \\  & XGBoost & 74.49 & 80.33 & 82.9 & 81.47 \\  & Logistic Regression & 66.99 & 76.83 & 73.31 & 74.81 \\  & SVM & 65.63 & 75.75 & 72.29 & 73.67 \\ \hline \multirow{6}{*}{Minimal-Redundancy-Maximal-Relevance} & Random Forest & 76.17 & 80.55 & 85.4 & 82.86 \\  & LGBM & 73.13 & 79.33 & 81.92 & 80.46 \\  & CatBoost & 73.12 & 79.83 & 80.88 & 80.21 \\  & XGBoost & 71.42 & 79.63 & 78.37 & 78.75 \\  & SVM & 66.32 & 76.56 & 72.86 & 74.58 \\  & Logistic Regression & 65.98 & 77.78 & 70.35 & 73.7 \\ \hline \multirow{6}{*}{Chi-Square} & Random Forest & 75.51 & 79.64 & 85.9 & 82.53 \\  & CatBoost & 73.12 & 80.1 & 80.36 & 80.04 \\  & LGBM & 72.43 & 79.51 & 79.87 & 79.54 \\  & XGBoost & 67.34 & 77.39 & 72.85 & 74.93 \\  & Logistic Regression & 64.63 & 76.58 & 70.33 & 72.85 \\  & SVM & 63.94 & 74.6 & 71.33 & 72.69 \\ \hline \multirow{6}{*}{Forward Feature Selection with Stochastic Gradient Descent} & Random Forest & 73.48 & 78.44 & 83.94 & 81.03 \\  & XGBoost & 71.78 & 79.05 & 79.41 & 79.18 \\  & CatBoost & 71.43 & 77.25 & 81.91 & 79.42 \\  & LGBM & 71.43 & 77.63 & 81.4 & 79.35 \\  & SVM & 66.67 & 77.05 & 72.36 & 74.59 \\  & Logistic Regression & 66.0 & 76.55 & 71.87 & 74.11 \\ \hline \multirow{6}{*}{Without Feature Selection} & CatBoost & 70.72 & 77.21 & 80.37 & 78.57 \\  & XGBoost & 69.71 & 76.83 & 78.85 & 77.23 \\ \cline{1-1}  & Random Forest & 68.34 & 73.73 & 82.91 & 77.99 \\ \cline{1-1}  & LGBM & 67.32 & 74.24 & 78.36 & 75.91 \\ \cline{1-1}  & SVM & 66.31 & 75.74 & 75.33 & 74.59 \\ \cline{1-1}  & Logistic Regression & 64.27 & 75.72 & 71.32 & 72.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance metrics of machine learning models with various feature selection methods. Logistic Regression once again struggled with lower accuracy rates (63.93% and 64.63%, respectively), highlighting the limitations of chi-squared when applied to linear models. Forward Feature Selection using SGD performed similarly for tree-based models like CatBoost, Random Forest, and XGBoost, with accuracy scores hovering between 71.43% and 73.48%. However, linear models like the SVM and Logistic Regression underperformed, achieving accuracy scores of 66%. This indicates that forward selection, which adds features incrementally, may not work as well for linear models when the feature space is complex, as it cannot exploit complex feature interactions that non-linear models can. Without feature selection, CatBoost achieved the highest accuracy (70.72%), followed closely by XGBoost (69.71%) and Random Forest (68.34%). However, these models struggled with precision and recall, indicating that the models were not adequately capturing the complex patterns in the data. The SVM and Logistic Regression performed worst, likely because these linear models are more sensitive to irrelevant or redundant features in high-dimensional data, such as EEG signals, where selecting key features can be critical to performance.