基于三段式特征选择策略的脑电情感识别算法SEE

doi:10.13229/j.cnki.jdxbgxb20210115

Abstract

Abstract:

Emotions can be recognized through the hidden patterns in the EEG signals. The large number of EEG features extracted based on numerous EEG channels makes the task of emotion recognition very complex. To solve above problems， An EEG emotion recognition algorithm (SEE) based on three-stage feature selection strategy is proposed. EEG features were systematically extracted from time domain， frequency domain and spatial domain in this study. Based on the extracted EEG feature set， the features with no significant difference between classes were removed by t-test firstly， and then the recursive feature elimination strategy was used to select target related features. Finally， the final feature set was determined by the sequential backward feature selection strategy for the emotion recognition. The experimental results show that the model constructed in this study has better emotion recognition ability than other methods. Compared with the existing feature selection algorithms， SEE can filter out better feature subset with a low time complexity. In addition， emotion-associated EEG channels and frequency bands were detected. The experimental results show physiological significance of emotion， which may facilitate the development of emotion-targeted EEG devices.

Key words: computer application, electroencephalogram, emotion recognition, feature engineering, feature selection

CLC Number:

TP391

Feng-feng ZHOU,Hai-yang ZHU. SEE： sense EEG⁃based emotion algorithm via three⁃step feature selection strategy[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1834-1841.

Figures/Tables 8

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方法	准确率	精确率	召回率	F1分数
基线	58.0	-	-	-
文献［13］	69.6	-	-	-
SEE-LR	70.2	72.4	81.4	76.7
SEE-SVM （Linear）	69.5	72.4	79.5	75.8
SEE-SVM （RBF）	71.0	74.2	79.3	76.7

方法	准确率	精确率	召回率	F1分数
基线	62.0	-	-	-
文献［13］	67.7	-	-	-
SEE-LR	69.5	72.6	78.4	75.3
SEE-SVM （Linear）	67.4	69.3	79.7	74.1
SEE-SVM （RBF）	70.0	72.5	78.8	75.5

方法	准确率	宏精确率	宏召回率	宏F1分数
文献［14］	85.5	-	-	-
文献［15］	87.0	-	-	-
文献［16］	90.6	-	-	-
SEE-LR	91.8	89.4	87.4	88.3
SEE-SVM（Linear）	90.2	88.1	86.3	86.1
SEE-SVM（RBF）	92.5	90.5	88.5	89.5

通道数	准确率/%
14	90.4
62	91.8

SEE： sense EEG⁃based emotion algorithm via three⁃step feature selection strategy

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