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Related Experiment Video

Updated: Sep 27, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

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A Study of Subliminal Emotion Classification Based on Entropy Features.

Yanjing Shi^1,2, Xiangwei Zheng¹, Min Zhang¹

¹School of Information Science and Engineering, Shandong Normal University, Jinan, China.

Frontiers in Psychology

|April 11, 2022

Summary

This study classifies unconscious emotions using electroencephalogram (EEG) signals. Wavelet packet energy and entropy proved effective features, with improved random forest achieving the highest accuracy in recognizing subliminal happiness and anger.

Keywords:

EEG feature extraction improved random forest subliminal emotion subliminal emotion classification

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Area of Science:

Neuroscience
Cognitive Science
Machine Learning

Background:

Emotion recognition is crucial in understanding human behavior.
Psychology categorizes emotions into conscious and unconscious states.
Electroencephalogram (EEG) is a key tool for analyzing brain activity related to emotions.

Purpose of the Study:

To classify subliminal emotions (happiness and anger) using EEG signals.
To identify effective signal processing features for distinguishing unconscious emotional states.
To compare the performance of different machine learning algorithms for this classification task.

Main Methods:

EEG signals were elicited by subliminal face stimulation.
Features extracted included multi-scale sample entropy (MSpEn), wavelet packet energy (E), and wavelet packet entropy (WpEn).
Classifiers used were the decision tree and an improved random forest algorithm.

Main Results:

Wavelet packet energy (E) and wavelet packet entropy (WpEn) showed higher classification accuracy than MSpEn.
The improved random forest algorithm achieved superior classification accuracy compared to the decision tree.
The study demonstrated the effectiveness of E and WpEn in classifying subliminal emotions.

Conclusions:

Wavelet packet energy and entropy are effective features for classifying subliminal emotions from EEG data.
The improved random forest algorithm offers a robust method for unconscious emotion recognition.
Findings provide physiological evidence for the subliminal affective priming effect.