基于有记忆递归神经网络的脑电特征情感识别研究

doi:10.16180/j.cnki.issn1007-7820.2020.11.013

Abstract

Abstract:

In order to improve the accuracy rate of the emotional recognition of EEG signals in multi-classification, the SEED dataset published by SJTU is selected as the sample of EEG dataset. The original EEG signal is divided into five frequency bands, and their features are extracted. After the features of the differential entropy, the differential asymmetry and the rational asymmetry of EEG datasets are smoothed by linear dynamic system, the classification effect is compared with the feature of power spectral density. Then, the method of the long short-term memory neural network is used to classify emotion. It is concluded that the classification of the differential entropy feature is effective. Finally, compared with other machine learning methods, the recognition rate is improved, and the average accuracy of emotion recognition reaches 95.045 9%.

Key words: EEG signals, SEED dataset, differential entropy, differential asymmetry, rational asymmetry, linear dynamical system, long short-term memory neural network

CLC Number:

TP391

ZHANG Yue,HU Chunyan. Research on Emotion Recognition of EEG Features Based on the Long Short-term Memory Neural Network[J].Electronic Science and Technology, 2020, 33(11): 67-72.

Figures/Tables 9

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References 20

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特征	delta	theta	alpha	beta	gamma
DE	79.39%	85.62%	90.17%	95.43%	97.8%
PSD	57.42%	60.69%	66.84%	68.02%	75.33%
ASM	59.35%	63.98%	68.4%	69.19%	76.75%

分类算法	平均准确率
KNN	62.13%
SVM	75.03%
CNN	87.55%
LSTM	95.04%

文献	分类方法	平均识别率/%
[17]	BDAE	91.01%
[18]	HCNN	88.20%
[19]	DBN	86.65%
[20]	集成CNN	93.12%

Research on Emotion Recognition of EEG Features Based on the Long Short-term Memory Neural Network

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