## 1 Introduction  
Brain-computer interfaces (BCIs) provide a means to develop interactions between humans and computers [1]. Within BCIs, affective BCIs are identified, and aim to detect users' emotional states using electroencephalographic (EEG) signals [2]. BCI devices record brain responses using various invasive and non-invasive acquisition techniques [3]. EEG monitored via external BCIs is non-invasive because it does not damage tissues. The use of BCIs for monitoring EEG is one of the preferred methods for emotion recognition due to its speed, low cost [2], and strong connection with emotional states [4]. Therefore, emotions can be recognized through the classification of incoming EEG signals.  
Brain signal studies have gained importance in recent years, because they are unlikely to intentionally fake brain activity [5]. However, the reliability of the results is still subject to various noise sources and potential inaccuracies inherent to the acquisition process. In general, the implementation of EEG-based emotion recognition has a wide range of potential applications in entertainment, therapy, security, business, and education.  
Emotion recognition primarily relies on combining features and models obtained from classifiers [6]. Thus, evaluating different types of characteristics is essential and necessary for developing more accurate emotion recognition systems. However, the challenge lies in determining which features are suitable for ensuring acceptable accuracy in terms of emotion recognition. There is no consensus on the EEG features for emotion detection to improve classification accuracy. Consequently, EEG emotion recognition has become an active topic in affective computing.  
In this work, we review different feature extraction methods in the time domain, frequency domain, time-frequency, and location (spatial) domains to obtain input vectors for classifiers such as the support vector machine (SVM), k-nearest neighbor (k-NN), and artificial neural networks (ANNs). The data to be analyzed come from the public access dataset DEAP [7; 8], which correlate emotional states with EEG signals using audio and images as stimuli, acquiring their data through BCI devices.  
Based on the EEG data from the DEAP dataset, sixteen feature vectors are constructed, some within a specific domain and others using multi-domain characteristics. These features are input to simple machine learning classifiers (SVM, k-NN, ANN). The resulting models are evaluated to define attributes that provide more information for classification and, thus, yield a better performance.  
The rest of the article is structured as follows: in Section 2, several related works found in the literature are presented. Section 3 explains the materials and methods employed in this research, including emotion models, DEAP dataset description, feature extraction in time, frequency, time-frequency, and location (spatial) domains, and classifiers. Section 4 provides details of the implementation and evaluation of the emotion recognition system in the four quadrants of valence-arousal (VA). Section 5 presents a discussion emerging from the results of the present study and various works in the literature. Finally, Section 6 outlines the conclusions and directions for future work.