Research on Emotion Recognition Based on EEG Time-Frequency-Spatial Multi-Domain Feature Fusion

被引：0

作者：

Wang, Lu ^{[1
]}

Liang, Mingjing ^{[1
]}

Shi, Huiyu ^{[1
]}

Wen, Xin ^{[1
]}

Cao, Rui ^{[1
]}

机构：

[1] Department of Software, Taiyuan University of Technology, Taiyuan

来源：

Computer Engineering and Applications | 2024年 / 59卷 / 04期

关键词：

CNN-BLSTM; electroencephalogram（EEG）; emotion recognition; feature extraction;

D O I：

10.3778/j.issn.1002-8331.2109-0083

中图分类号：

学科分类号：

摘要：

The traditional emotion recognition based on electroencephalogram（EEG）mainly adopted a single EEG feature extraction approach. In order to make full use of the rich information contained in EEG, a new method of EEG emotion recognition based on multi-domain feature fusion is proposed. This paper extracts EEG features in time-domain, frequency-domain and space-domain, and fuses the three domain features as the input of the emotion recognition model. Firstly, the power spectral density of the three frequency bands of alpha, beta and gamma of the EEG signal in different time windows are calculated, and combined with the spatial information of the EEG electrode, the EEG images are formed. Then, the convolutional neural network（CNN）and bidirectional long short-term memory network（BLSTM）are used to construct the CNN-BLSTM model for emotion recognition, and the features of time, frequency and space domains are learned respectively. The method is verified in the SEED dataset. The results show that the method can effectively improve the accuracy of recognition, and the average recognition accuracy is 96.25%. © 2024 Chinese Journal of Animal Science and Veterinary Medicine Co., Ltd.. All rights reserved.

引用

页码：191 / 196

页数：5

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