## 2 Related Work  
Several studies on emotion recognition have been found in the literature, addressing classification based on the two-dimensional model categorizing valence/arousal (VA) in terms of the categories derived from EEG signals from the DEAP dataset. Some studies in the literature begin with the classification of the categories of high/low valence (HV and LV) and high/low arousal (HA and LA) for emotion recognition. For example, [PERSON] et al. studied emotion recognition by implementing features such as entropy, statistics, fractal dimension, power functions, and wavelet, which correspond to time, frequency, and time-frequency, respectively. The authors achieved average accuracy results in the valence dimension of 75.06% with the k-NN classifier and 77.62% with the SVM classifier. In comparison, in arousal, they obtained average results of 74.71% and 78.96% for k-NN and SVM, respectively. The authors conclude that the time domain statistical features better distinguish between low and high valence and arousal levels [9]. The authors of [10] propose selecting locally robust EEG features (LRFS) for emotion recognition. In feature extraction, they employ methods such as power ratio and power difference, statistical techniques like variance, kurtosis, and skewness, and variables like the zero-crossing rate and Shannon entropy; using these methods, they obtained a total of 364 EEG features. The authors achieved average accuracy results in Arousal of 60.05%, 65.10%, and 61.85%. The average accuracy in valence was 65.13%, 67.97%, and 65.60% using the k-NN, SVM, and ANN classifiers, respectively. The authors implement features in time and frequency domains for feature extraction without establishing which features improve classifier performance. In [11], the authors propose emotion recognition by implementing the spectral power method for individual channels and spectral power asymmetry among the 12 pairs of electrodes for feature extraction from the five frequency bands (delta, theta, alpha, beta, and gamma) of the EEG signals. These methods correspond to the frequency domain. The authors obtained average accuracy values of 65.0% for Arousal and 63.3% for Valence using the SVM classifier with a radial basis function (RBF) kernel. Using the naive Bayes classifier, they achieved an average accuracy of 65.6% in arousal and 68.0% in valence. However, their approach does not determine which feature extraction methods better classify emotional states. In [12], the authors propose feature extraction from EEG signals by implementing statistical procedures (median, standard deviation, kurtosis), Hjorth parameters, the spectral density of frequency bands, fractal dimension, and wavelets. Additionally, they incorporate feature selection using the minimum redundancy maximum relevance (mRMR) method for emotional state recognition in the VA space. The authors report accuracy results of 60.72% in arousal and 62.39% in valence with the SVM classifier, using an RBF kernel. The authors implement feature extraction methods in time, frequency, and time-frequency without concluding which features yield the best results. Other authors perform emotion recognition based on the four quadrants of the two-dimensional model; that is, they classify the quadrants of the VA model into high arousal-high valence (HAHV), high arousal-low valence (HALV), low arousal-low valence (LALV), and low arousal-high valence (LAHV), which implies greater complexity, given the number of classes to classify. For example, the authors of [13] propose exploring multi-domain features to identify the attributes that contribute to ranking a higher number of emotion classes. The authors implement feature extraction methods, such as Hjorth parameters and entropy, Fourier transform, RASM, DASM, and discrete wavelet transform and differential entropy. The authors achieved an average accuracy of 65.72% with time-domain features using the SVM classifier with a polynomial kernel. However, they explore a minimal set of feature extraction methods.