Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (8): 1834-1841.doi: 10.13229/j.cnki.jdxbgxb20210115

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

Feng-feng ZHOU1,2(),Hai-yang ZHU1,2   

  1. 1.College of Computer Science and Technology,Jilin University,Changchun 130012,China
    2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University,Changchun 130012,China
  • Received:2021-02-05 Online:2022-08-01 Published:2022-08-12

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

Table 1

Experimental results on valence dimension of DEAP dataset"

方法准确率精确率召回率F1分数
基线58.0---
文献[1369.6---
SEE-LR70.272.481.476.7
SEE-SVM (Linear)69.572.479.575.8
SEE-SVM (RBF)71.074.279.376.7

Table 2

Experimental results on arousal dimension of DEAP dataset"

方法准确率精确率召回率F1分数
基线62.0---
文献[1367.7---
SEE-LR69.572.678.475.3
SEE-SVM (Linear)67.469.379.774.1
SEE-SVM (RBF)70.072.578.875.5

Table 3

Experimental results of SEED dataset"

方法准确率宏精确率宏召回率宏F1分数
文献[1485.5---
文献[1587.0---
文献[1690.6---
SEE-LR91.889.487.488.3
SEE-SVM(Linear)90.288.186.386.1
SEE-SVM(RBF)92.590.588.589.5

Fig.1

Performance comparison with other feature selection algorithms"

Fig.2

Running time comparison with other feature selection algorithms"

Fig.3

Contributions of different frequency bands"

Fig.4

Spatial distributions of EEG electrodes closely associated with emotions"

Table 4

Experimental result with different number of EEG channels in SEED dataset"

通道数准确率/%
1490.4
6291.8
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