# An Ensemble Feature Selection Approach to Identify Relevant Features from EEG Signals
## 3 Results
### Selecting Relevant Features from the EEG Dataset  
This phase used EPS to analyze the relevance of features on a dataset with descriptions of normal and abnormal segments extracted from EEGs. To validate the subset of relevant features calculated using the EFS method, a classification process was built to evaluate the accuracy reached with the subset of features selected. The setting of the aggregation method of the EFS returned the features aggregated from the subsets of relevant features generated by each FS algorithm with an importance index greater than or equal to 0.7. This setting was established experimentally following the trial-and-error approach. Thus, we tested different hyperparameters for the FS algorithms and different thresholds for the aggregation; the selected threshold (0.7) was the importance index used to select the subset of relevant features that allow to build the classifier with the best performance. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(K\) & **Features Selected** & \multicolumn{2}{c}{**SVM**} \\ \hline  & & **All** & **EFS** \\ \hline
1 & 3 & 87.69 \(\pm\) 1.17 & 94.63 \(\pm\) 1.5 \\
3 & 10 & 87.54 \(\pm\) 1.15 & 94.93 \(\pm\) 1.4 \\
5 & 17 & 87.61 \(\pm\) 1.2 & 95.97 \(\pm\) 1.2 \\
7 & 23 & 87.69 \(\pm\) 1.07 & 96.61 \(\pm\) 1.07 \\
9 & 27 & 87.29 \(\pm\) 1.2 & 96.79 \(\pm\) 1.05 \\
15 & 35 & 87.06 \(\pm\) 1.2 & 97.31 \(\pm\) 1.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy results—SVM. \begin{table}
\begin{tabular}{c c c c} \hline \hline \(K\) & **Features Selected** & \multicolumn{2}{c}{**Random Forest**} \\ \hline  & & **All** & **EFS** \\ \hline
1 & 3 & 89.17 \(\pm\) 2.2 & 87.84 \(\pm\) 2.03 \\
3 & 10 & 89.2 \(\pm\) 2.76 & 88.17 \(\pm\) 3.13 \\
5 & 17 & 89.32 \(\pm\) 2.6 & 88.79 \(\pm\) 2.6 \\
7 & 23 & 89.24 \(\pm\) 2.7 & 89.24 \(\pm\) 2.26 \\
9 & 27 & 89.31 \(\pm\) 2.8 & 89.72 \(\pm\) 2.6 \\
15 & 35 & 89.45 \(\pm\) 2.12 & 89.81 \(\pm\) 2.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy results—random forest. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Subset** \\ \hline SelectKBest1 & ‘F11’, ‘F15’, ‘F28’, ‘F32’, ‘F36’, ‘F40’, ‘F49’, ‘F53’, ‘F57’, ‘F61’, ‘F70’, ‘F74’, ‘F78’, ‘F95’, ‘F99’ \\ SelectKBest2 & ‘F1’, ‘F55’, ‘F60’, ‘F65’, ‘F71’, ‘F76’, ‘F77’, ‘F85’, ‘F86’, ‘P92’, ‘F97’, ‘F106’, ‘F118’, ‘F126’, ‘F132’ \\ SelectKBest3 & ‘F1’, ‘F55’, ‘F60’, ‘F65’, ‘F71’, ‘F76’, ‘F77’, ‘F85’, ‘F86’, ‘P92’, ‘F97’, ‘F106’, ‘F118’, ‘F126’, ‘F132’ \\ RFE & ‘F11’, ‘F15’, ‘F28’, ‘F32’, ‘F36’, ‘F53’, ‘F70’, ‘F74’, ‘F78’, ‘F95’, ‘F99’, ‘F116’, ‘F120’, ‘F140’, ‘F141’ \\ Feature Importance & ‘F97’, ‘F6’, ‘P92’, ‘F118’, ‘R85’, ‘F114’, ‘F126’, ‘F77’, ‘F107’, ‘F119’, ‘F106’, ‘F18’, ‘R65’, ‘F64’ \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Subsets of features selected by single algorithms. The best results of classification were reached using the subset of 27 relevant features selected by the EFS method: 'F1', 'F11', 'F15', 'F28', 'F32', 'F36', 'F40', 'F53', 'F55', 'F60', 'F65', 'F70', 'F71', 'F74', 'F76', 'F77', 'F78', 'F85', 'F86', 'F92', 'F95', 'F97', 'F99', 'F106', 'F118', 'F126', and 'F132'. It is important to mention that if the threshold defined was 0, the subset of relevant features would include the union of the subsets generated by each FS algorithm. In this case, the final subset of relevant features would contain 39 features: 'F1', 'F11', 'F15', 'F18', 'F19', 'F28', 'F32', 'F36', 'F40', 'F49', 'F53', 'F55', 'F57', 'F60', 'F61', 'F64', 'F65', 'F70', 'F71', 'F74', 'F76', 'F77', 'F78', 'F85', 'F86', 'F92', 'F95', 'F97', 'F99', 'F106', 'F107', 'F114', 'F116', 'F118', 'F120', 'F126', 'F132', 'F140', and 'F141'. Table 6 shows the results in the classification of a decision tree (DT) algorithm, linear regression (LR) algorithm, random forest (RF) algorithm, and support vector machine algorithm using all features, and the features calculated by the select \(K\) best algorithm, recursive feature elimination algorithm, feature importance algorithm, and EFS method.