# Emotional State Detection Using Electroencephalogram Signals: A Genetic Algorithm Approach
## 5 Conclusions  
In this work, a multi-objective GA statistical model with a specific search strategy for variable selection was proposed and tested in a high-dimensional EEG signal database for emotional classification. The proposed model allowed a reduction in the dimensionality of the data of 98.08%. This model, compared to that used in similar studies, outperforms the dimensionality reduction sought, which could enhance the feasibility of developing specific-purpose devices for real-time applications in future works. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Autor’s** & **Overall Accuracy** & **Observations** \\ \hline This Study & 95.87\% & 49 out of 2548 features selected with genetic algorithms and ANN ML model for classification. 44 out of 2100 features were used with classification models such as Bayesian networks, support vector machine and random forest, the last one being the most accurate. 63 out of 2548 features selected via information gain measurement. Classification method: random forest ensemble classifier. 256 out of 2479 features selected based on information gain measurement from gray-scale image representation of statistical features. \\ [PERSON] et al. [27] & 89.38\% & 63 out of 2548 features selected via information gain measurement. Classification method: random forest ensemble classifier. 256 out of 2479 features selected based on information gain measurement from gray-scale image representation of statistical features. \\ [PERSON] et al. [27] & 89.38\% & 63 out of 2548 features selected based on information gain measurement from gray-scale image representation of statistical features. Classification method: deep convolutional neural network. \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of this study with previous similar studies. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**GA Feature Selection** & **KNN** & **RF** & **ANN** \\ \hline
**Overall Accuracy** & 90.43\% & 93.43\% & 95.87\% \\ \hline
**Sequential Feature Selection** & **KNN** & **RF** & **ANN** \\ \hline
**Overall Accuracy** & 74.11\% & 92.68\% & 84.98\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: GA and SFS feature selection comparison. The proposed model reduced the 2548 features describing the waves related to different emotional states, to only 49 optimal features, which were used to create machine learning (ML) classification models with algorithms such as k-nearest neighbor (KNN), random forests (RF) and artificial neural networks (ANN), obtaining results with 90.06%, 93.62% and 95.87% accuracy, respectively, which are higher than the 87.16% and 89.38% accuracy of previous works. Furthermore, according to our forward selection analysis, it can be inferred that by sacrificing a little accuracy without going below 90%, emotional states could be described with only eight features or 0.31% of the total of 2548 features contained in the database used, which was a reduction unlike any other published in similar studies. Additionally, a comparison was made between the selection method used and the widely validated method: sequential feature selection of the SciKit-Learn library ([[https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)]([https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)), accessed on 9 May 2023). From this comparison, it can be observed that GALGO has better results. As shown in Table 8, the three algorithms that used the features selected using GALGO obtained better results than the algorithms that used the features selected with SFS did, so we can validate the feature selection methodology proposed in this research and affirm that GALGO works very well and obtains better models than the traditional ones do. The proposed methodology generated a model with a promising approach for detecting emotions in real time using only 49 features extracted from EEG signals.