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

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 cl...

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Vydáno v:Mathematical biosciences and engineering : MBE Ročník 21; číslo 3; s. 4779 - 4800
Hlavní autoři: Zhang, Xiaodan, Wang, Shuyi, Xu, Kemeng, Zhao, Rui, She, Yichong
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States AIMS Press 29.02.2024
Témata:
cross-subject
dtn
emotion recognition
lmn
ssa-rf
SSA-RF
cross-subject
LMN
emotion recognition
DTN
ISSN:1551-0018, 1551-0018
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1551-0018
1551-0018
DOI:10.3934/mbe.2024210