A Dual-Branch Dynamic Graph Convolution Based Adaptive TransFormer Feature Fusion Network for EEG Emotion Recognition

Mingyi Sun · Weigang Cui · Shuyue Yu · Hongbin Han · Bin Hu · Yang Li

135 citations0/0 files storedIEEE Transactions on Affective Computing

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Abstract

Electroencephalograph (EEG) emotion recognition plays an important role in the brain-computer interface (BCI) field. However, most of recent methods adopted shallow graph neural networks using a single temporal feature, leading to the limited emotion classification performance. Furthermore, the existing methods generally ignore the individual divergence between different subjects, resulting in poor transfer performance. To address these deficiencies, we propose a dual-branch dynamic graph convolution based adaptive transformer feature fusion network with adapter-finetuned transfer learning (DBGC-ATFFNet-AFTL) for EEG emotion recognition. Specifically, a dual-branch graph convolution network (DBGCN) is firstly designed to effectively capture the temporal and spectral characterizations of EEG simultaneously. Second, the adaptive Transformer feature fusion network (ATFFNet) is conducted by integrating the obtained feature maps with the channel-weight unit, leading to significant difference between different channels. Finally, the adapter-finetuned transfer learning method (AFTL) is applied in cross-subject emotion recognition, which proves to be parameter-efficient with few samples of the target subject. The competitive experimental results on three datasets have shown that our proposed method achieves the promising emotion classification performance compared with the state-of-the-art methods. The code of our proposed method will be available at:https://github.com/smy17/DANet.

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References

2011DEAP: A Database for Emotion Analysis ;Using Physiological Signals 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 2017DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals From Wireless Low-cost Off-the-Shelf Devices 2014Feature Extraction and Selection for Emotion Recognition from EEG 2017Identifying Stable Patterns over Time for Emotion Recognition from EEG 2020EEG-Based Emotion Recognition Using Regularized Graph Neural Networks

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2024Light-weight residual convolution-based capsule network for EEG emotion recognition 2024A comprehensive review of deep learning in EEG-based emotion recognition: classifications, trends, and practical implications 2024MAS-DGAT-Net: A dynamic graph attention network with multibranch feature extraction and staged fusion for EEG emotion recognition 2024MASA-TCN: Multi-Anchor Space-Aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition

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