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[Electroencephalogram emotion recognition based on state-space models combined with spatio-temporal feature]

Chunli Wang1, Jiahao Zhang1, Jiarong Dou1

  • 1School of Electronic and Information Engineering, Lanzhou Jiao tong University, Lanzhou 730070, P. R. China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|April 27, 2026
PubMed
Summary

This study introduces ST-Mamba, a novel architecture for electroencephalogram (EEG)-based emotion recognition, significantly improving accuracy and cross-subject generalization by effectively processing spatiotemporal features and frequency band information.

Keywords:
Deep learningElectroencephalogramElectroencephalogram signal processingEmotion recognitionMamba

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State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
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