Cross-subject EEG-based emotion recognition through dynamic optimization of random forest with sparrow search algorithm

Xiaodan Zhang; Shuyi Wang; Kemeng Xu; Rui Zhao; Yichong She; Xiaodan Zhang; Shuyi Wang; Kemeng Xu; Rui Zhao; Yichong She

doi:10.3934/mbe.2024210

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 3: 4779-4800. doi: 10.3934/mbe.2024210

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Research article

Cross-subject EEG-based emotion recognition through dynamic optimization of random forest with sparrow search algorithm

1.
School of Electronics and Information, Xi'an Polytechnic University, Xi'an, Shaanxi 710060, China
2.
School of Life Sciences, Xi Dian University, Xi'an, Shaanxi 710126, China

Academic Editor: Vladimir Mityushev

Received: 18 December 2023 Revised: 03 February 2024 Accepted: 18 January 2024 Published: 29 February 2024

The objective of EEG-based emotion recognition is to classify emotions by decoding signals, with potential applications in the fields of artificial intelligence and bioinformatics. Cross-subject emotion recognition is more difficult than intra-subject emotion recognition. The poor adaptability of classification model parameters is a significant factor of low accuracy in cross-subject emotion recognition. We propose a model of a dynamically optimized Random Forest based on the Sparrow Search Algorithm (SSA-RF). The decision trees number (DTN) and the leave minimum number (LMN) of the RF are dynamically optimized by the SSA. 12 features are used to construct feature combinations for selecting the optimal feature combination. DEAP and SEED datasets are employed for testing the performance of SSA-RF. The experimental results show that the accuracy of binary classification is 76.81% on DEAP, and the accuracy of triple classification is 75.96% on SEED based on SSA-RF, which are both higher than that of traditional RF. This study provides new insights for the development of cross-subject emotion recognition, and has significant theoretical value.
- cross-subject,
- emotion recognition,
- SSA-RF,
- DTN,
- LMN
Citation: Xiaodan Zhang, Shuyi Wang, Kemeng Xu, Rui Zhao, Yichong She. Cross-subject EEG-based emotion recognition through dynamic optimization of random forest with sparrow search algorithm[J]. Mathematical Biosciences and Engineering, 2024, 21(3): 4779-4800. doi: 10.3934/mbe.2024210

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Abstract

The objective of EEG-based emotion recognition is to classify emotions by decoding signals, with potential applications in the fields of artificial intelligence and bioinformatics. Cross-subject emotion recognition is more difficult than intra-subject emotion recognition. The poor adaptability of classification model parameters is a significant factor of low accuracy in cross-subject emotion recognition. We propose a model of a dynamically optimized Random Forest based on the Sparrow Search Algorithm (SSA-RF). The decision trees number (DTN) and the leave minimum number (LMN) of the RF are dynamically optimized by the SSA. 12 features are used to construct feature combinations for selecting the optimal feature combination. DEAP and SEED datasets are employed for testing the performance of SSA-RF. The experimental results show that the accuracy of binary classification is 76.81% on DEAP, and the accuracy of triple classification is 75.96% on SEED based on SSA-RF, which are both higher than that of traditional RF. This study provides new insights for the development of cross-subject emotion recognition, and has significant theoretical value.

References

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