# EEG Emotion Classification Based on Graph Convolutional Network
###### Abstract  
EEG-based emotion recognition is a task that uses scalp-EEG data to classify the emotion states of humans. The study of EEG-based emotion recognition can contribute to a large spectrum of application fields including healthcare and human-computer interaction. Recent studies in neuroscience reveal that the brain regions and their interactions play an essential role in the processing of different stimuli and the generation of corresponding emotional states. Nevertheless, such regional interactions, which have been proven to be critical in recognizing emotions in neuroscience, are largely overlooked in existing machine learning or deep learning models, which focus on individual channels in brain signals. Motivated by this, in this paper, we present RGNet, a model that is designed to learn the regional level representation of EEG signal for accurate emotion recognition. Specifically, after applying preprocessing and feature extraction techniques on raw signals, RGNet adopts a novel region-wise encoder to extract the features of channels located within each region as input to compute the regional level features, enabling the model to effectively explore the regional functionality. A graph is then constructed by considering each region as a node and connections between regions as edges, upon which a graph convolutional network is designed with spectral filtering and learned adjacency matrix. Instead of focusing on only the spatial proximity, it allows the model to capture more complex functional relationships. We conducted experiments from the perspective of region division strategies, region encoders and input feature types. Our model has achieved 98.64% and 99.33% for Deep and Dreamer datasets, respectively. The comparison studies show that RGNet outperforms the majority of the existing models for emotion recognition from EEG signals. elecroencephalogram; deep learning; emotion classification +
Footnote †: journal: Medical Imaging  
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Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote †: journal: Medical Imaging  
exemplifying the importance of conducting research on emotion recognition using EEG signals. Nowadays, the processing and classification on EEG data still remains challenging. Essentially, EEG signal is the observation of source signal that has been sent from deeper brain regions that varies with time and is transmitted with different intensity. This non-stationary property makes it difficult to be handled by linear methods [5]. Moreover, EEG signal can be contaminated with noise and artifacts produced by the external environment or mixed with other signals. These issues may affect the collection of brain signals, making it challenging to handle emotion classification by naive methods. Therefore, more and more studies are seeking machine learning or deep learning approaches to solve this task. Earlier studies primarily emphasize how to extract temporal features from an individual channel, where the inter-channel activity was not thoroughly explored [6; 7; 8]. Recently, there have been many works proposed to utilize the fused features of multiple channels. For instance, some studies utilizing convolutional neural networks (CNNs) combine the signal with adjacent channels by treating electrodes as equally spaced pixels in images; another widely used approach is graph neural networks (GNNs), which map channels into nodes and relationships into edges to probe the topological characteristics of brain activity. Although these formulations provide a way to describe how activities from different locations occur coherently by channel-level representation, it still does not guarantee to capture the complex functional relationship occurring at the regional level, which has been identified to be an important factor in the elicitation of emotion.