Bi-Branch Vision Transformer Network for EEG Emotion Recognition

Abstract

Electroencephalogram (EEG) signals have emerged as an important tool for emotion research due to their objective reflection of real emotional states. Deep learning-based EEG emotion classification algorithms have made preliminary progress, but existing models struggle with capturing long-range dependence and integrating temporal, frequency, and spatial domain features to limit their classification ability. To address these challenges, this study proposes a Bi-branch Vision Transformerbased EEG emotion recognition model, Bi-ViTNet, that integrates spatial-temporal and spatial-frequency feature representations. Specifically, Bi-ViTNet is composed of spatial-frequency feature extraction branch and spatial-temporal feature extraction branch, which can fuse spatial-frequency-temporal features in a unified framework. Each branch is composed of Linear Embedding and Transformer Encoder, which is used to extract spatial-frequency features and spatial-temporal features. Finally, fusion and classification are performed by the Fusion and Classification layer. Experiments on SEED and SEED-IV datasets demonstrate that Bi-ViTNet outperforms state-of-the-art baselines.

EEG-based emotion recognition using graph convolutional neural network with dual attention mechanism

“…Additionally, humans may involuntarily or intentionally conceal their real emotions through facial expressions and language except EEG signals ( Zhang et al, 2020 ). As a result, researchers prefer emotion recognition methods based on EEG signals as they are more reliable and objective in capturing an individual’s emotional state ( Lu et al, 2023 ).…”