Emotion recognition of EEG signals based on variational mode decomposition and weighted cascade forest

Dingxin Xu; Xiwen Qin; Xiaogang Dong; Xueteng Cui; Dingxin Xu; Xiwen Qin; Xiaogang Dong; Xueteng Cui

doi:10.3934/mbe.2023120

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 2: 2566-2587. doi: 10.3934/mbe.2023120

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Research article

Emotion recognition of EEG signals based on variational mode decomposition and weighted cascade forest

Dingxin Xu ¹,
Xiwen Qin ^{1
,
,},
Xiaogang Dong ^{1
,
,},
Xueteng Cui ^{1,2
,
,}

1.
School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China
2.
Academic Affairs Office, Changchun University, Changchun 130022, China
Academic editor: Hamidreza Namazi

Received: 06 September 2022 Revised: 27 October 2022 Accepted: 31 October 2022 Published: 24 November 2022

Emotion recognition is of a great significance in intelligent medical treatment and intelligent transportation. With the development of human-computer interaction technology, emotion recognition based on Electroencephalogram (EEG) signals has been widely concerned by scholars. In this study, an EEG emotion recognition framework is proposed. Firstly, variational mode decomposition (VMD) is used to decompose the nonlinear and non-stationary EEG signals to obtain intrinsic mode functions (IMFs) at different frequencies. Then sliding window tactic is used to extract the characteristics of EEG signals under different frequency. Aiming at the issue of feature redundancy, a new variable selection method is proposed to improve the adaptive elastic net (AEN) by the minimum common redundancy maximum relevance criterion. Weighted cascade forest (CF) classifier is constructed for emotion recognition. The experimental results on the public dataset DEAP show that the valence classification accuracy of the proposed method reaches 80.94%, and the classification accuracy of arousal is 74.77%. Compared with some existing methods, it effectively improves the accuracy of EEG emotion recognition.
- electroencephalogram,
- adaptive elastic net with minimum common redundancy maximum relevance,
- variable selection,
- cascade forest,
- emotion recognition
Citation: Dingxin Xu, Xiwen Qin, Xiaogang Dong, Xueteng Cui. Emotion recognition of EEG signals based on variational mode decomposition and weighted cascade forest[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 2566-2587. doi: 10.3934/mbe.2023120

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Abstract

Emotion recognition is of a great significance in intelligent medical treatment and intelligent transportation. With the development of human-computer interaction technology, emotion recognition based on Electroencephalogram (EEG) signals has been widely concerned by scholars. In this study, an EEG emotion recognition framework is proposed. Firstly, variational mode decomposition (VMD) is used to decompose the nonlinear and non-stationary EEG signals to obtain intrinsic mode functions (IMFs) at different frequencies. Then sliding window tactic is used to extract the characteristics of EEG signals under different frequency. Aiming at the issue of feature redundancy, a new variable selection method is proposed to improve the adaptive elastic net (AEN) by the minimum common redundancy maximum relevance criterion. Weighted cascade forest (CF) classifier is constructed for emotion recognition. The experimental results on the public dataset DEAP show that the valence classification accuracy of the proposed method reaches 80.94%, and the classification accuracy of arousal is 74.77%. Compared with some existing methods, it effectively improves the accuracy of EEG emotion recognition.

References

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