# EEG Feature Extraction Using Genetic Programming for the Classification of Mental States
## 6 Conclusions and Future Work  
This paper proposes a new system for the classification of two mental states based on EEG recordings, with the introduction of a novel GP algorithm that performs as a feature extraction method. The proposed system involves several functional blocks: acquisition of signals, pre-processing, feature extraction, and classification. This work is the first to use a GP-based algorithm for feature extraction for the identification of mental states (specifically, normal and relaxed states). A reference system was implemented based on our previous work [9], which produced competitive results among similar research works in this domain. Nevertheless, the proposed system outperformed our previous results by further exploiting the separability of classes using evolved transformation models. Certainly, apart from the appropriate choice of the classifier, one of the \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Name** & \multicolumn{4}{c}{**Training**} & \multicolumn{4}{c}{**Testing**} \\ \hline  & & **Specificity** & **Recall** & **F-Score** & **Accuracy** & **Specificity** & **Recall** & **F-Score** & **Accuracy** \\ \hline LDA & C1 & 0.8014 & 0.8201 & 0.8308 & & 0.7993 & 0.7684 & 0.7713 & \\  & C2 & 0.8201 & 0.8014 & 0.8002 & & 0.7684 & 0.7993 & 0.8001 & \\  & Average & 0.8108 & 0.8108 & 0.8155 & \(81.9\pm 2.5\) & 0.7838 & 0.7838 & 0.7887 & \(78.8\pm 16.2\) \\ \hline TB & C1 & 1.0000 & 1.0000 & 1.0000 & & 0.7552 & 0.7134 & 0.6852 & \\  & C2 & 1.0000 & 1.0000 & 1.0000 & & 0.7134 & 0.7552 & 0.7203 & \\  & Average & 1.0000 & 1.0000 & 1.0000 & & 0.7343 & 0.7047 & \(70.6\pm 15.6\) \\ \hline RF & C1 & 1.0000 & 1.0000 & 1.0000 & & 0.7751 & 0.7082 & 0.6795 & \\  & C2 & 1.0000 & 1.0000 & 1.0000 & & 0.7082 & 0.7751 & 0.7205 & \\  & Average & 1.0000 & 1.0000 & 1.0000 & & 0.7417 & 0.7417 & 0.7000 & \(70.2\pm 16.3\) \\ \hline k-NN & C1 & 1.0000 & 1.0000 & 1.0000 & & 0.6768 & 0.6131 & 0.6118 & \\  & C2 & 1.0000 & 1.0000 & 1.0000 & & 0.6131 & 0.6768 & 0.6483 & \\  & Average & 1.0000 & 1.0000 & 1.0000 & 100 \(\pm\) 0 & 0.6449 & 0.6449 & 0.6300 & \(63.2\pm 11.1\) \\ \hline SVM & C1 & 0.9627 & 0.9500 & 0.9587 & & 0.6533 & 0.6759 & 0.6334 & \\  & C2 & 0.9570 & 0.9627 & 0.9607 & & 0.6759 & 0.6533 & 0.6232 & \\  & Average & 0.9598 & 0.9598 & 0.9597 & \(95.9\pm 1.3\) & 0.6646 & 0.6646 & 0.6283 & \(64.2\pm 13.8\) \\ \hline \hline \multicolumn{2}{l}{\(p\)-value} & \multicolumn{4}{c}{**0.0000**} & \multicolumn{4}{c}{0.0687} \\ \hline \hline \end{tabular}
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Table 6: Classifier performance for the \({}^{+}\)FEGP algorithm. Values for specificity, recall, and F-score represent means over 13 folds; the accuracies also highlight the standard deviation. C1 refers to the normal state and C2 refers to the relaxed state. The last row shows the one-way Analysis of Variance (ANOVA) test between classifiers in terms of the accuracy; bold values indicate that the null hypothesis is rejected at an \(\alpha=0.05\) significance level. \begin{table}
\begin{tabular}{l l c} \hline \hline
**Classifier** & **Parameter** & **Value** \\ \hline Tree Bagger (TB) & Number of trees & 200 \\  & Number of observations per leaf & 5 \\  & Number of predictors to sample at each node & 6 \\ \hline Random Forests (RF) & Number of trees & 300 \\  & Minimum node size & 5 \\  & Number of predictors sampled for splitting at & 4 \\  & each node & \(k\)-Means \\ \hline k-Nearest Neighbors (k-NN) & Search method & \(k\)-Means tree \\  & Maximum number of data points in the leaf & 50 \\  & node & \(k\)-Means \\  & Number of nearest neighbors & 8 \\  & Distance metric & Manhattan \\  & Distance weighting function & None \\ \hline Support Vector Machine (SVM) & Kernel function & Gaussian \\  & Kernel scale & 56.78 \\  & Kernel offset & 0 \\  & Box constraint & 994.49 \\  & Optimization routine & Sequential minimal \\  & optimization \\ \hline \hline \end{tabular}
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Table 5: Summary of the parameters and hyperparameters used for the additional classifiers.