A Multi-Dimensional Graph Convolution Network for EEG Emotion Recognition

Guanglong Du · Jinshao Su · Linlin Zhang · Kang Su · Xueqian Wang · Shaohua Teng +1 more

95 citations0/0 files storedIEEE Transactions on Instrumentation and Measurement

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Emotion and Mood Recognition EEG and Brain-Computer Interfaces Face and Expression Recognition

Abstract

Due to the changeable, high-dimensional, non-stationary, and other characteristics of electroencephalography (EEG) signals, the recognition of EEG signals is mostly limited to independent individuals. To deal with these issues, we propose a multi-dimensional graph convolution network (MD-GCN), which integrates EEG signals’ temporal and spatial characteristics and can classify emotions more accurately. First, we use that the asymmetry of neuron activity in the left and right hemispheres is very important for emotion prediction to initialize the adjacency matrix and perform preliminary edge prediction without considering node features. Then, we perform the feature fusion on the Inception network and then input it into the graph convolution network to learn the interrelationship between channels. Finally, we visually analyze the adjacency matrix. To evaluate the performance of the model, we conduct experiments on the SEED dataset and the SEED-IV dataset. The results show that the pre-defined adjacency matrix method can improve the accuracy of emotion recognition and the graph convolution has better performance than the same type of convolution. It also theoretically shows that the emotional state is mainly by the interaction of important brain regions.

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References

2015Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks 2018EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks 2018EmotionMeter: A Multimodal Framework for Recognizing Human Emotions 2013Differential entropy feature for EEG-based emotion classification 2020EEG-Based Emotion Recognition Using Regularized Graph Neural Networks 2020EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM 2022Comparing Recognition Performance and Robustness of Multimodal Deep Learning Models for Multimodal Emotion Recognition 2019GCB-Net: Graph Convolutional Broad Network and Its Application in Emotion Recognition

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2024MAS-DGAT-Net: A dynamic graph attention network with multibranch feature extraction and staged fusion for EEG emotion recognition 2023Functional connectivity-enhanced feature-grouped attention network for cross-subject EEG emotion recognition 2024Bridge Graph Attention Based Graph Convolution Network With Multi-Scale Transformer for EEG Emotion Recognition 2023EmotionKD: A Cross-Modal Knowledge Distillation Framework for Emotion Recognition Based on Physiological Signals

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