## 1 Introduction
However, such existing approaches still have some limitations. First, the research results of EEG-based emotion recognition using deep learning methods still lag behind those of image and speech recognition. Therefore, to develop a more accurate and reliable emotion recognition model, an improved deep learning model that reflects EEG characteristics is required. Second, according to neurological studies, human emotions are closely related to various areas of the cerebral cortex, such as the amygdala, frontal lobe, and parietal lobe [26; 27], and spatiotemporal information from different brain regions helps us understand human emotions [28; 29; 30]. However, these neurological research results are not sufficiently reflected. Because the contribution of EEG signals related to each brain region is different, a method that can utilize the spatiotemporal information of different brain regions is required to understand human emotional states. Third, information about spatial resolution, brain region separation, brain network analysis, individual differences in emotions, and capturing dynamic changes in emotions, which multichannel EEG includes, is not fully utilized. To this end, new studies have recently attempted to utilize the advantages of multichannel EEG fully to identify activities in specific regions of the brain and perform more accurate and multifaceted emotion recognition through the interaction and information combination among brain regions. However, the number of studies is extremely small. [PERSON] et al. [31] used a hierarchical spatiotemporal EEG feature learning model with attention-based antagonism neural network, whereas [PERSON] et al. [32] proposed a transformer-based model to hierarchically learn the discriminative spatial information from the electrode level to the brain-region level. Motivated by these research results and limitations, a more accurate and reliable emotion recognition model was developed by reflecting EEG characteristics based on neurological studies. In this paper, we propose an EEG-based emotion recognition method using a hierarchical spatiotemporal context feature learning model (HSCFLM) that extracts local and global EEG features by robustly learning the spatiotemporal dependencies within and among brain regions. * The main contributions of this paper are:
* To understand the activity of certain brain regions based on human emotions and to enable the interaction among brain regions and the combination of information, we propose a hierarchical neural network model structure with self-attention. The hierarchical deep neural network model with self-attention comprises a regional brain-level encoding module and a global brain-level encoding module;
* To obtain activity weights for each brain region according to emotional state, a regional brain-level encoding module was designed based on a dual-stream parallel double BGRU encoder with self-attention (2 BGRUA) for extracting spatiotemporal EEG features. In particular, the spatial encoder learns the interchannel correlations and the temporal encoder captures the temporal dependencies of the time sequence of the EEG channels. Subsequently, the output of each encoder can be integrated to obtain the local spatiotemporal EEG features;
* Next, the global spatiotemporal encoding module uses a single BGRU-based self-attention (BGRUA) to integrate important information within various brain regions to improve the emotion classification performance by learning discriminative spatiotemporal EEG features from local brain regions to the entire brain region. Thus, the influence of brain regions with a high contribution is strengthened by the learned weights, and the influence of the less dependent regions is reduced. The remainder of this paper is organized as follows: Section 2 introduces the proposed method, Section 3 describes the experimental data and results, and Section 4 presents the conclusions of the study and future research directions.