Evolutionary Ensemble Learning for EEG-Based Cross-Subject Emotion Recognition
Electroencephalogram (EEG) has been widely utilized in emotion recognition due to its high temporal resolution and reliability. However, the individual differences and non-stationary characteristics of EEG, along with the complexity and variability of emotions, pose challenges in generalizing emotio...
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| Veröffentlicht in: | IEEE journal of biomedical and health informatics 2024-07, Vol.28 (7), p.3872-3881 |
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| creator | Zhang, Hanzhong Zuo, Tienyu Chen, Zhiyang Wang, Xin Sun, Poly Z.H. |
| description | Electroencephalogram (EEG) has been widely utilized in emotion recognition due to its high temporal resolution and reliability. However, the individual differences and non-stationary characteristics of EEG, along with the complexity and variability of emotions, pose challenges in generalizing emotion recognition models across subjects. In this paper, an end-to-end framework is proposed to improve the performance of cross-subject emotion recognition. A novel evolutionary programming (EP)-based optimization strategy with neural network (NN) as the base classifier termed NN ensemble with EP (EPNNE) is designed for cross-subject emotion recognition. The effectiveness of the proposed method is evaluated on the publicly available DEAP, FACED, SEED, and SEED-IV datasets. Numerical results demonstrate that the proposed method is superior to state-of-the-art cross-subject emotion recognition methods. The proposed end-to-end framework for cross-subject emotion recognition aids biomedical researchers in effectively assessing individual emotional states, thereby enabling efficient treatment and interventions. |
| doi_str_mv | 10.1109/JBHI.2024.3384816 |
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However, the individual differences and non-stationary characteristics of EEG, along with the complexity and variability of emotions, pose challenges in generalizing emotion recognition models across subjects. In this paper, an end-to-end framework is proposed to improve the performance of cross-subject emotion recognition. A novel evolutionary programming (EP)-based optimization strategy with neural network (NN) as the base classifier termed NN ensemble with EP (EPNNE) is designed for cross-subject emotion recognition. The effectiveness of the proposed method is evaluated on the publicly available DEAP, FACED, SEED, and SEED-IV datasets. Numerical results demonstrate that the proposed method is superior to state-of-the-art cross-subject emotion recognition methods. 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| subjects | Adaptation models Adult Algorithms Biological neural networks Brain modeling Cross-subject transfer learning Data models Databases, Factual EEG Effectiveness electro- encephalogram Electroencephalography Electroencephalography - methods Emotion recognition Emotional factors Emotions Emotions - physiology Ensemble learning Evolutionary algorithms Feature extraction Female Humans Machine Learning Male Medical research Neural networks Neural Networks, Computer Pattern Recognition, Automated - methods Signal Processing, Computer-Assisted Temporal resolution |
| title | Evolutionary Ensemble Learning for EEG-Based Cross-Subject Emotion Recognition |
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