## 1 Introduction  
In recent years, affective computing has attracted significant interest among researchers from various fields, which requires human knowledge from multiple disciplines, including psychology, biology, and computer science [1, 2]. A major challenge in these areas is emotion recognition, which focuses on enabling computer systems to process, recognize, and understand human emotional expressions accurately. Moreover, emotions play a key role in the fields of brain-computer interaction and artificial intelligence. Advances in emotion recognition technology can propel the progress of numerous disciplines, such as computer science, robotics, psychology, neuroscience, medicine, education, entertainment, and criminal investigation [3]. Human emotion detection can be conducted using either non-physiological or physiological signals' data [4]. Non-physiological signals, such as facial expressions [5], are unreliable due to inaccurate emotions caused by illness or artificial control leading to unclear or incorrect signal acquisition. In contrast, physiological electroencephalograph (EEG) signals are generated naturally by the mental states and endocrine systems in response to emotions, which are resistant to human manipulation. EEG uses scalp electrodes to directly record brain signals, which are nonlinear, non-stationary, and noisy [6]. Indeed, EEG signals are considered a more effective yet challenging method for emotion classification due to characteristics of reliability, high temporal resolution, cost-effectiveness, and sensitivity to emotional changes. Obviously, the EEG are internal signals generated naturally from the brain without human interferences. Then, identification of the best method for emotion recognition is the big challenge for the biomedical experts. Moreover, EEG data collection presents various difficulties essentially due to specialized knowledge and high quality capture methods. It is clear that additional noise is existed in the collected EEG signals, which definitely reduces the signal quality. Because of the EEG complexity, the noise removal is researched continuously. Another challenge is the input feature dimension reduction while maintaining relatively high performance related to emotion detection. Furthermore, the selection of affective classifier is also an important challenge. Indeed, a small but effective input features and the optimal classifier produce better emotion detection performance and are easily applied in the practical environment [7]. Emotion recognition using the EEG signals employs discrete and dimensional models in which the former sorts emotions into positive and negative categories [8], while the latter adopts the 2-dimensional valence-arousal Russell's circumplex model, which delimits emotions on a scale of varying emotional intensity ascending from left to right and bottom to top on the coordinate axis system [9]. Additionally, different human states are linked closely to brain activities in various regions and frequency bands. Therefore, electrodes are positioned at various locations on the scalp to capture EEG signals from the corresponding brain regions [2]. Furthermore, EEG signals originate from the electrical wave frequencies of the human brain, which range from 1 to 100 Hz corresponding to 5 distinct sub-bands of delta, theta, alpha, beta, and gamma [10]. In addition, the human brain is constructed from the cerebrum, cerebellum, and brain stem, in which the cerebellum is divided into the frontal, parietal, temporal, central, and occipital lobes. The individual lobes provide unique EEG signals, which are crucial for emotion recognition. As a result, the electrode order is labeled as low, medium, and high corresponding to the first 32, from 33 to 128, and higher than 128 electrodes in resolution, respectively [11]. The rapid advancements in Machine Learning (ML) and Deep Learning (DL) have promoted a large number of extensive studies on the effective designs of emotion detection applied in practical environments.