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. 2022 Mar 25:13:781448.
doi: 10.3389/fpsyg.2022.781448. eCollection 2022.

A Study of Subliminal Emotion Classification Based on Entropy Features

Yanjing Shi  1   2 , Xiangwei Zheng  1 , Min Zhang  1 , Xiaoyan Yan  3 , Tiantian Li  4 , Xiaomei Yu  1
Affiliations

A Study of Subliminal Emotion Classification Based on Entropy Features

Yanjing Shi et al. Front Psychol. .

Abstract

Electroencephalogram (EEG) has been widely utilized in emotion recognition. Psychologists have found that emotions can be divided into conscious emotion and unconscious emotion. In this article, we explore to classify subliminal emotions (happiness and anger) with EEG signals elicited by subliminal face stimulation, that is to select appropriate features to classify subliminal emotions. First, multi-scale sample entropy (MSpEn), wavelet packet energy (E i ), and wavelet packet entropy (WpEn) of EEG signals are extracted. Then, these features are fed into the decision tree and improved random forest, respectively. The classification accuracy with E i and WpEn is higher than MSpEn, which shows that E i and WpEn can be used as effective features to classify subliminal emotions. We compared the classification results of different features combined with the decision tree algorithm and the improved random forest algorithm. The experimental results indicate that the improved random forest algorithm attains the best classification accuracy for subliminal emotions. Finally, subliminal emotions and physiological proof of subliminal affective priming effect are discussed.

Keywords: EEG; feature extraction; improved random forest; subliminal emotion; subliminal emotion classification.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The process of subliminal emotion classification.
Figure 2
Figure 2
Energy ratio of 4-layer wavelet packet decomposition.
Figure 3
Figure 3
The principle of improved random forest algorithm.
Figure 4
Figure 4
Comparison of classification accuracy with three features and decision tree classifier.
Figure 5
Figure 5
Comparison of classification accuracy with three features and improved fandom forest.
Figure 6
Figure 6
Comparison of classification accuracy of two classifiers based on MSpEn.
Figure 7
Figure 7
Comparison of classification accuracy of two classifiers based on Ei.
Figure 8
Figure 8
Comparison of classification accuracy of two classifiers based on WpEn.
Figure 9
Figure 9
Comparison of the average classification results with other classifiers.
See this image and copyright information in PMC

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