## 1 Introduction  
Emotional recognition is considered an important topic in various fields, such as human-computer interaction, psychological research, and neuroscience [1, 2, 3, 4]. Emotions are crucial in daily life and significantly influence behavior, communication, thinking, and mental health [5]. Thus, if machines can recognize human emotions, they offer innovative potential in various applications, including designing more effective computer systems, providing personalized services, and diagnosing and treating mental health [6]. In early emotion research studies, numerous attempts were made to recognize emotions using external signals, such as facial expressions, voices, and behavioral patterns [7, 8, 9, 10]. However, these external signals can be judged differently depending on the subjective interpretation, and discrepancies may arise between outwardly expressed and actual inner emotions [11]. For this reason, with the development of noninvasive sensor technology, the research and application of emotion recognition using biosignals has recently attracted considerable attention [12]. Biological signals [13, 14] measure a person's physical condition, nervous system activity, and physiological responses, including electrocardiograms, skin conductivity, electromyography, and electroencephalography (EEG). Such biometric signalsmay reflect changes in the internal state of a person and provide information related to emotions and emotional reactions [15]. Among these biosignals, EEG, which is measured from the brain and is directly involved in the processing, generation, and control of emotions, is considered a crucial biosignal for emotion recognition and has several advantages over other modalities. EEG can directly evaluate brain areas related to emotions by directly measuring electrical activities in the brain, provides high temporal resolution to detect and analyze changes in emotions quickly, and can be applied to medical and clinical settings because it detects a wide range of emotions. In addition, emotion recognition using EEG is useful for personalized emotion recognition models and real-time emotion monitoring. Therefore, emotion recognition using EEG can understand and interpret an individual's internal state and emotions more accurately [16]. Initially, single-channel EEG was primarily used to record brain waves using a single electrode [17]. However, the need for multichannel EEG recordings to analyze the activities occurring in various brain regions has gradually emerged, and now multichannel EEG systems that record brain waves simultaneously using dozens of electrodes are widely used. In emotion recognition studies, multichannel EEG can be used to understand the brain's emotional processing mechanisms and develop more accurate and comprehensive emotion recognition systems by providing local activities associated with specific brain regions, network interactions among various brain regions, and individual differences and changes in emotions [18]. Deep learning technology, which has rapidly been developing and becoming commonplace in recent years, has achieved remarkable results in EEG-based emotion recognition, surpassing traditional machine learning methods [19; 20; 21; 22]. [PERSON] et al. [23] used a hierarchical convolutional neural network (CNN) for EEG-based emotion classification to hierarchically extract features contained in the spatial topology of electrodes that are neglected in a one-dimensional deep model, such as a stacked autoencoder. [PERSON] et al. [24] improved the emotion recognition performance using multichannel EEG signals by constructing a hybrid deep learning model that combined CNNs and recurrent neural networks (RNNs). The proposed model effectively captured the interchannel correlation and contextual information. [PERSON] et al. [25] proposed a hierarchical BGRU network with attention mechanism. This model reflects the hierarchical structure of EEG signals and learns important features by utilizing the attention mechanism at both the sample and epoch levels.