Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 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.

PubMed Disclaimer

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

Similar articles

See all similar articles

Cited by

References

    1. Aydın S.. (2019). Deep learning classification of neuro-emotional phase domain complexity levels induced by affective video film clips. IEEE J. Biomed. Health Inf. 24, 1695–1702. 10.1109/JBHI.2019.2959843 - DOI - PubMed
    1. Bai D., Qiu T., Li X. (2007). The sample entropy and its application in eeg based epilepsy detection. J. Biomed. Eng. 24, 200–205. 10.3321/j.issn:1001-5515.2007.01.043 - DOI - PubMed
    1. Bajaj V., Pachori R. B. (2014). Human emotion classification from eeg signals using multiwavelet transform, in International Conference on Medical Biometrics (Shenzhen: ).
    1. Bhatti A. M., Majid M., Anwar S. M., Khan B. (2016). Human emotion recognition and analysis in response to audio music using brain signals. Comput. Hum. Behav. 65, 267–275. 10.1016/j.chb.2016.08.029 - DOI
    1. Bo S.. (2017). Research on the classification of high dimensional imbalanced data based on the optimizational random forest algorithm, in International Conference on Measuring Technology & Mechatronics Automation (New York, NY: ).

LinkOut - more resources