research-article

ASTDF-Net: Attention-Based Spatial-Temporal Dual-Stream Fusion Network for EEG-Based Emotion Recognition

Authors:

Daoqiang ZhangAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 883 - 892

https://doi.org/10.1145/3581783.3612208

Published: 27 October 2023 Publication History

Abstract

Emotion recognition based on electroencephalography (EEG) has attracted significant attention and achieved considerable advances in the fields of affective computing and human-computer interaction. However, most existing studies ignore the coupling and complementarity of complex spatiotemporal patterns in EEG signals. Moreover, how to exploit and fuse crucial discriminative aspects in high redundancy and low signal-to-noise ratio EEG signals remains a great challenge for emotion recognition. In this paper, we propose a novel attention-based spatial-temporal dual-stream fusion network, named ASTDF-Net, for EEG-based emotion recognition. Specifically, ASTDF-Net comprises three main stages: first, the collaborative embedding module is designed to learn a joint latent subspace to capture the coupling of complicated spatiotemporal information in EEG signals. Second, stacked parallel spatial and temporal attention streams are employed to extract the most essential discriminative features and filter out redundant task-irrelevant factors. Finally, the hybrid attention-based feature fusion module is proposed to integrate significant features discovered from the dual-stream structure to take full advantage of the complementarity of the diverse characteristics. Extensive experiments on two publicly available emotion recognition datasets indicate that our proposed approach consistently outperforms state-of-the-art methods.

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Cited By

Zhou YJiang JWang LLiang SLiu H(2025)Enhanced Cognitive Load Detection in Air Traffic Control Operators Using EEG and a Hybrid Deep Learning ApproachIEEE Access10.1109/ACCESS.2025.353009113(12127-12137)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3530091
Dong YJing CMahmud MNg MWang S(2024)Enhancing cross-subject emotion recognition precision through unimodal EEG: a novel emotion preceptor modelBrain Informatics10.1186/s40708-024-00245-811:1Online publication date: 18-Dec-2024
https://doi.org/10.1186/s40708-024-00245-8
Liu QZhou ZWang JLiang ZCao ZJung TXu P(2024)Joint Contrastive Learning with Feature Alignment for Cross-Corpus EEG-based Emotion RecognitionProceedings of the 1st International Workshop on Brain-Computer Interfaces (BCI) for Multimedia Understanding10.1145/3688862.3689112(9-17)Online publication date: 28-Oct-2024
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Index Terms

ASTDF-Net: Attention-Based Spatial-Temporal Dual-Stream Fusion Network for EEG-Based Emotion Recognition
1. Computing methodologies
  1. Artificial intelligence
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods

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cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
,
Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chairs:
Marco Bertini
University of Florence, Italy
,
Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
,
Pradeep K. Atrey
University at Albany, State University of New York, USA
,
M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

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Published: 27 October 2023

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the National Natural Science Foundation of China
the Key Research and Development Plan of Jiangsu Province
the China Postdoctoral Science Foundation

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MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

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https://doi.org/10.1109/ACCESS.2025.3530091
Dong YJing CMahmud MNg MWang S(2024)Enhancing cross-subject emotion recognition precision through unimodal EEG: a novel emotion preceptor modelBrain Informatics10.1186/s40708-024-00245-811:1Online publication date: 18-Dec-2024
https://doi.org/10.1186/s40708-024-00245-8
Liu QZhou ZWang JLiang ZCao ZJung TXu P(2024)Joint Contrastive Learning with Feature Alignment for Cross-Corpus EEG-based Emotion RecognitionProceedings of the 1st International Workshop on Brain-Computer Interfaces (BCI) for Multimedia Understanding10.1145/3688862.3689112(9-17)Online publication date: 28-Oct-2024
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