Author(s)

Zhonggao Li ¹, Yanping Cai ¹, Tao Wang ¹, Wan Chen ¹

Affiliation(s)

¹ Room 305, Rocket Force University of Engineering, Xi'an, China

Corresponding Author

Yanping Cai

ABSTRACT

EEG feature extraction is very important for emotion recognition. In order to better extract useful features and improve recognition accuracy, an emotion recognition method based on VMD and FuzzyEn was proposed. Firstly, the original signal was decomposed into five rhythm waves by wavelet transform, and then the selected rhythm waves were decomposed into variational mode. Fuzzy entropy features were extracted from the decomposed variational mode functions, and then the features were fused, and the feature sorting and selection were carried out by Fscore feature selection method. Finally, the SVM classification model was used for emotion classification. Experiments were carried out on the first group of data in SEED-Ⅳ data set. The results showed that more refined classification of signals was helpful to eliminate redundant information and improve the accuracy of emotion classification, and feature fusion had higher classification accuracy than single feature.

KEYWORDS

Variational Mode Decomposition, Fuzzy Entropy, SVM

CITE THIS PAPER

Zhonggao Li, Yanping Cai, Tao Wang, Wan Chen, EEG Emotional Feature Extraction Method Based on VMD-FuzzyEn. Journal of Artificial Intelligence Practice (2023) Vol. 6: 1-7. DOI: http://dx.doi.org/10.23977/jaip.2023.060301.

REFERENCES

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