## 2 Background  
In general, the field of Affective Computing has shown significant interest in using physiological cues to assess emotional states [4]. Some psychological and physiological research has suggested a connection between physiological processes and the way we perceive emotions. Therefore, Computational Psychophysiology-based approaches are seen as valuable additions to recognition methods that rely on non-physiological cues like facial expressions or speech. These non-physiological cues can be less reliable, especially when individuals intentionally conceal their true emotions. Given that the brain regulates the autonomic nervous system during emotional experiences, exploring the use of brain activity information, such as EEG, to understand emotional cognition mechanisms and detect emotional states is a particularly promising area of study [5]. Research has shown a direct connection between the central nervous system and emotional processes. EEG serves as an external indicator of the brain's cognitive psychological functions, making it a valuable tool for studying emotional psychology and developing emotion-recognition technology. However, it is important to construct recognition models that include EEG data from all significant frequency bands and cortex regions, as described by [6]. EEG-based emotion recognition is a subset of pattern-recognition research, and it follows a structured process to address recognition challenges [5]. In pattern recognition, several fundamental steps are involved. First, there is the essential task of defining and quantifying the recognition goal, transforming it into a computable problem. Second, obtaining high-quality research data is crucial because it forms the basis for effectively defining models' decision boundaries. Third, data preprocessing and representation, including feature extraction or feature learning, play an essential role in building pattern recognition models. These steps ensure that relevant characteristics are extracted from raw data while eliminating extraneous information. Finally, the iterative process of designing, training, and evaluating recognition models based on processed data continues until an acceptable level of recognition accuracy is achieved. EEG-based emotion recognition uses this structured approach to understand emotional states from brainwave data [7]. Multiple studies have emphasized the significance of frequency domain attributes and functional connectivity (FC) in identifying depression using resting-state Electroencephalography (rsEEG) [8; 9]. Studying rsEEG features has the potential to reveal complex neural mechanisms associated with depression. The rise of computational psychiatry has amplified interest in using rsEEG-based machine learning methods to identify disease patterns, laying a foundation for clinical depression diagnosis [10]. As described by [11], mental disorders like depression and bipolar disorder can cause the body to release cortisol, a stress hormone, into the brain. Cortisol can affect the production and communication of neurons, which can slow down the function of certain parts of the brain and change the electrical activity patterns. The voltage variations resulting from ionic current flows within the neurons of the brain may be useful for diagnosing mental disorders such as depression and bipolar disorder. According to [12], the amplitude of brain responses (ABR) in the central, frontal, and parietal regions could potentially serve as biomarkers for differentiating patients with depression from healthy individuals. Brain activity changes when brain function deteriorates, which can be detected using EEG brain scans. EEG is a valuable tool due to its affordability, accessibility, and high temporal resolution, allowing measurement of brain activity in milliseconds. However, EEG signals are complex, non-stationary, and non-linear, which makes manual interpretation challenging. Automated signal-processing techniques enable the accurate classification of individuals with depression compared to those without.