## 3 Materials and Methods
### Data Acquisition  
We have considered the public dataset commonly used for emotion analysis: the EEG signal set of the DEAP dataset.  
DEAP is a dataset containing multiple physiological signals with emotional evaluations, including EEG signals from 32 subjects (16 men and 16 women) while watching 40 one-minute video clips with different emotional tendencies.  
The EEG signals are recorded at a sampling frequency of 512 Hz to activate 32 active electrodes according to the international 10-20 system [17]. Each participant evaluates their valence, arousal, dominance, and liking levels using the Self-Assessment Manikin (SAM) [18]. Each data file (s01.dat-s32.dat) contains two matrices:  
* Data matrix: 40 \(\times\) 40 \(\times\) 8064, where the first 40 elements represent the total number of videos, the second 40 represent the collection of signals from the total number of channels (32 EEG), and 8064 are the experimental data based on the video and sampling sequence (63 \(\times\) 128). The first 3 s correspond to the reference data obtained before the experiment. The last 60 s are the information recorded during the experiment.
* Label matrix: 40 \(\times\) 4, where the number of videos used is represented by 40, and 4 is the number of labels describing the affective dimensions: valence, arousal, dominance, and liking, with scores ranging from 1.0 to 9.0 according to the SAM (Self-Assessment Manikin) scale.  
Figure 1: Steps in the proposed emotion recognition process.  
According to the two-dimensional emotion model (see Figure 2), the emotions labeled in each video can be classified into 4 quadrants based on the valence-arousal space. Valence is on the \(x\)-axis, and arousal is on the \(y\)-axis. Therefore, there are four quadrants: high arousal-high valence (HAHV), low arousal-high valence (LAHV), high arousal-low valence (HALV), and low arousal-low valence (LALV). Arousal scales range from passive to active, and valence ranges from negative to positive.  
In this bi-dimensional representation, the emotions are described using valence, i.e., positive or negative feelings. Arousal refers to the degree of physical or mental activation or energy. High arousal is the sensation that generates high energy, and low arousal is the sensation of low energy. For instance, terror, stress, and anger are negative emotions that produce high arousal. Sadness, depression, lethargy, and fatigue are states with negative valence and low arousal. Euphoria, happiness, and enthusiasm are positive-valence and high-arousal emotions. Calm, placidity, and satisfaction are positive-valence and low-arousal feelings.  
In the present work, we evaluate valence and arousal levels from preprocessed EEG signal recordings of the 32 channels at a downsampled sampling frequency of 128 Hz. Each trial contains 8064 data points for the sampling frequency, resulting in a 40 \(\times\) 32 \(\times\) 8064 structure corresponding to the number of trials per participant, the number of channels, and the stored signal data for each channel.  
The emotions that could be differentiated according to their ubication in a quadrant in the VA space are, for example, terror, anger, anxiety/ephoria, enthusiasm, happiness/boredom, apathy, sadness/tranquility, placidity, and satisfaction. These emotions correspond to various combinations of valence and arousal levels within the VA space, allowing for an understanding of a person's emotional state.