Global Context MambaVision for EEG-based Emotion Recognition

Emotion recognition tasks based on physiological signals require the simultaneous capture of local features and global correlations of these signals. Although transformer-based models are widely used due to their superior ability to integrate information, their quadratic computational complexity lim...

Full description

Saved in:
Bibliographic Details
Published inProceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 1 - 5
Main Authors Wang, Hao, Xu, Li, Yu, Yuntao, Ding, Weiyue, Xu, Yiming
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.04.2025
Subjects
Attention mechanisms
Brain modeling
Computational efficiency
Computational modeling
Context modeling
Electroencephalogram
Emotion recognition
Generators
Mamba
Physiology
Speech recognition
Time complexity
Transformer
Online AccessGet full text

Cover

More Information
Summary:Emotion recognition tasks based on physiological signals require the simultaneous capture of local features and global correlations of these signals. Although transformer-based models are widely used due to their superior ability to integrate information, their quadratic computational complexity limits their efficiency in processing large-scale or high-resolution data. Recently, state space models (SSM) with efficient hardware-aware designs have demonstrated significant potential in modeling long sequences. However, existing SSMs face limitations in processing global information due to window constraints. Therefore, this paper introduces a novel Global Context (GC) MambaVision model, which combines the linear time complexity advantage of SSMs with a new type of local-global attention mechanism. GC MambaVision maintains high computational efficiency in emotion recognition tasks while providing a more comprehensive understanding of the dynamic changes in local and global emotional states. Experimental results on the DEAP and SEED-V datasets show that GC MambaVision achieves superior performance compared to current state-of-the-art models, with accuracies reaching 98.62% and 85.88%, respectively.
ISSN:2379-190X
DOI:10.1109/ICASSP49660.2025.10890602