# Emotional State Detection Using Electroencephalogram Signals: A Genetic Algorithm Approach
For feature selection, the best result was obtained with the OneR algorithm with 44 features selected from a set of 2100, which were used with classification models such as Naive Bayes, Bayesian networks, random tree, support vector machine, multilayer perceptron and random forest, the last one being the most accurate with a prediction accuracy of 87.16%. In a following study, [PERSON] et al. [26] compared single and ensemble methods for classifying emotions from an EEG brainwave database, and they also compare feature selection methods such as OneR, Bayes network, information gain, and symmetrical uncertainty. From 2548 characteristics, 63 were chosen with the information gain method and their higher results of classification were found with the random forest ensemble classifier with an overall accuracy of 97.89%. The best single classifier was a deep neural network with an accuracy of 94.89%. Ten-fold cross validation was used for training the models. In their study, [PERSON] and collaborators [27] classified EEG signals with an approach based on the representation of the statistical features from the signals as images. In their work, they reduce a large set of features containing 2479 features to a set only containing 256 using the algorithm of information gain measure; then, they used the resulting features to reshape them and express them as grayscale images which were then used in a deep convolutional neural network to train the data and classify the mental state of the subject with an accuracy of 89.38%. For the validation of this study, the data were split in a 70/30 ratio for training and testing, respectively. Moreover, [PERSON] et al. [28] proposed a special feature extraction methodology using the empirical mode decomposition (EMD) domain mixed with sequence backward selection (SBS) in order to remove the reductant features. Additionally they compared different temporal windows' lengths and the kinds of rhythms of EEG signals, the one with a length of 1 s being the one achieving the highest recognition accuracy of 86.46% in valence and 84.90% in arousal. In this work, the dataset samples were split into an 80/20 test and training ratio. On the other hand, [PERSON] and collaborators [29], using their approach, studied the effects of selecting different channels and frequency bands of EEG signals on the precision of emotion recognition. Initially, they used the discrete wavelet transform method to separate the signals into bands such as gamma, beta, alpha, and theta; then, the entropy and energy were extracted from each band as the class features. Afterwards, they evaluated channel combinations and compared them using three methods, the first one based on experience, the second based on the indirect minimal redundancy maximal relevance (mRMR-FS) algorithm, and the third one based on the direct channel selection method mRMR-CS algorithm, using each channel as a whole. In their results, of the three methods used, mRMR-CS had the best ability to reduce channels, reaching an accuracy of 79.46% and reducing the number of channels from 32 to 22. This study proposes the use of intelligent algorithms to perform temporal and statistical feature selection from EEG brain waves related to different emotional states, with the use of genetic algorithms (GA) and KNN, RF and ANN machine learning models to classify three emotional states with improved accuracy. Section 2 discusses the materials and methods used in this research work. Section 3 provides the results. Section 4 shows the discussion, and Section 5 reports the conclusions. The main contributions of this study are outlined below. The proposed model enabled the dimensionality of the data to be reduced by 98.08%. Compared to similar studies, this dimensionality reduction is superior to others, and could improve the feasibility of developing specific-purpose devices for real-time applications in feature jobs.