Emotion Recognition from Multi-channel EEG Data through A Dual-pipeline Graph Attention Network
Loading...
Access rights
openAccess
URL
Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Authors
Date
2021
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
6
3642-3647
3642-3647
Series
Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Abstract
EEG based emotion recognition technology is currently an important concept in artificial intelligence, and also holds great potential in emotional health care. Nevertheless, one major limitation of the prior approaches is they do not capture the relationships between different time-series and channels explicitly, resulting in inevitable low performance, especially in subject-independent recognition settings. In this paper, we propose a novel graph attention network based model to address this issue. Our framework includes dual-pipeline Graph Attention Network layers in parallel to learn the complex dependencies of multi-channel EEG in both temporal and spatial dimensions. The proposed method outperforms other state-of-the-art models on benchmark SEED dataset. Further analysis shows that our method also has good interpretability. As far as we know, it is the first work that introduce graph attention network into EEG based emotion detection research.Description
Funding Information: This work is supported by the Major Science and Technology Innovation Projects of Key R&D Programs of Shandong Province in 2019 (grant No.2019JZZY010113) Publisher Copyright: © 2021 IEEE.
Keywords
EEG, emotion recognition, graph attention network, mental health
Other note
Citation
Li, X, Li, J, Zhang, Y & Tiwari, P 2021, Emotion Recognition from Multi-channel EEG Data through A Dual-pipeline Graph Attention Network . in Y Huang, L Kurgan, F Luo, X T Hu, Y Chen, E Dougherty, A Kloczkowski & Y Li (eds), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 . IEEE, pp. 3642-3647, IEEE International Conference on Bioinformatics and Biomedicine, Virtual, Online, United States, 09/12/2021 . https://doi.org/10.1109/BIBM52615.2021.9669544