Label-refined EEG emotion classification based on intelligent media communication

Zitian Fu; Qiao Sun

doi:10.54254/2755-2721/88/20241638

Research Article

Open access

Published on 27 August 2024

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Fu,Z.;Sun,Q. (2024). Label-refined EEG emotion classification based on intelligent media communication. Applied and Computational Engineering,88,72-80.

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Label-refined EEG emotion classification based on intelligent media communication

Zitian Fu ¹, Qiao Sun *^,2,

¹ Central South University
² Central South University

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/88/20241638

Abstract

With the development of cognitive computing and intelligent media communication, recognizing the user’s emotion using electroencephalography (EEG) has garnered increasing attention. However, building a cross-subject emotion recognition model with good generalization performance is really difficult. To achieve remarkable performance, a label-refined domain adversarial neural network is designed and EEG samples are subdivided into 6 groups according to the differences in users' brain activities under different movie stimuli. Based on the experiment of SEED and SEED-IV datasets, the classification accuracy of our method reaches 93.35±4.14% and 84.08±6.01%, achieving the state-of-the-art performance. Experiments show that using different movie stimuli to refine labels can achieve efficient cross-subject emotion recognition.

Keywords

EEG emotion recognition, label refinement, domain adversarial neural network

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Cite this article

Fu,Z.;Sun,Q. (2024). Label-refined EEG emotion classification based on intelligent media communication. Applied and Computational Engineering,88,72-80.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 6th International Conference on Computing and Data Science

Conference website: https://2024.confcds.org/

ISBN：978-1-83558-603-7(Print) / 978-1-83558-604-4(Online)

Conference date: 12 September 2024

Editor：Alan Wang, Roman Bauer

Series: Applied and Computational Engineering

Volume number: Vol.88

ISSN：2755-2721(Print) / 2755-273X(Online)

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