EEG emotion analysis based on LSTM-Transformer
10.3969/j.issn.1005-202X.2024.12.013
- VernacularTitle:基于LSTM-Transformer的脑电情感分析
- Author:
Anqi WANG
1
;
Chao YU
;
Yinwei CHEN
;
Qun XI
Author Information
1. 甘肃省中医药大学信息工程学院,甘肃 兰州 730000
- Publication Type:Journal Article
- Keywords:
electroencephalogram;
emotion recognition;
deep learning;
long short-term memory;
Transformer
- From:
Chinese Journal of Medical Physics
2024;41(12):1550-1557
- CountryChina
- Language:Chinese
-
Abstract:
An electroencephalogram(EEG)emotion analysis model(LTNet)that combines long short-term memory(LSTM)and Transformer modules is proposed for addressing the shortcomings of traditional emotion recognition methods in dealing with long-term dependencies.After data preprocessing,the extracted features are input into LTNet.LSTM module and Transformer module model the input sequence independently,and from which deep local features and global features are extracted and then fused using a weighted fusion strategy.Finally,Softmax function is used to classify emotions into 4 categories.Experimental results show that LTNet has an average recognition accuracy of 96.56%in the 5-fold cross-validation on the DEAP dataset,which is 2.74%-21.31%higher than traditional machine learning algorithms and other deep learning methods.
