Bibliographic Details
| Title: |
Bi-Branch Vision Transformer Network for EEG Emotion Recognition |
| Authors: |
Wei Lu, Tien-Ping Tan, Hua Ma |
| Source: |
IEEE Access, Vol 11, Pp 36233-36243 (2023) |
| Publisher Information: |
IEEE, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| Subject Terms: |
Affective computing, EEG-based emotion recognition, transformer, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| More Details: |
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 encouraging progress, but existing models struggle with capturing long-range dependence and integrating temporal, frequency, and spatial domain features that limit their classification ability. To address these challenges, this study proposes a Bi-branch Vision Transformer- based 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 that 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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2169-3536 |
| Relation: |
https://ieeexplore.ieee.org/document/10098561/; https://doaj.org/toc/2169-3536 |
| DOI: |
10.1109/ACCESS.2023.3266117 |
| Access URL: |
https://doaj.org/article/ad5ec0ec49ba4f4cb2b6bbd4d40802ae |
| Accession Number: |
edsdoj.5ec0ec49ba4f4cb2b6bbd4d40802ae |
| Database: |
Directory of Open Access Journals |
