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Related Experiment Video

Updated: Dec 9, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Single-trial EEG emotion recognition using Granger Causality/Transfer Entropy analysis.

Yunyuan Gao1, Xiangkun Wang2, Thomas Potter3

  • 1Intelligent Control and Robotics Institute, College of Automation, Hangzhou Dianzi University, Hangzhou, China; Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China.

Journal of Neuroscience Methods
|September 8, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for emotion recognition by combining Histogram of Oriented Gradient (HOG) with Granger Causality (GC) or Transfer Entropy (TE) for improved electroencephalogram (EEG) analysis. The novel approach significantly enhances classification accuracy for stress and calm states.

Keywords:
ElectroencephalogramEmotion recognitionGranger CausalityHistogram of oriented gradientTransfer Entropy

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Area of Science:

  • Neuroscience
  • Computer Science
  • Signal Processing

Background:

  • Emotion recognition from electroencephalogram (EEG) signals is a long-standing challenge.
  • Existing methods often rely on single-channel EEG features, limiting classification accuracy.

Purpose of the Study:

  • To develop a novel emotion recognition approach by integrating advanced feature extraction techniques.
  • To improve the accuracy of classifying emotional states like stress and calm.

Main Methods:

  • A new method combining Histogram of Oriented Gradient (HOG) with Granger Causality (GC) or Transfer Entropy (TE) was developed.
  • HOG was used to extract features from GC/TE relationship matrices.
  • Support Vector Machine (SVM) was employed for classification of emotional states.

Main Results:

  • The proposed method achieved high classification accuracies: 88.93% for GC-HOG and 95.21% for TE-HOG.
  • The inclusion of HOG feature extraction resulted in approximately a 12% increase in accuracy compared to methods without HOG.

Conclusions:

  • Combining Transfer Entropy (TE) with Histogram of Oriented Gradient (HOG) is feasible for enhanced emotion recognition.
  • The approach effectively utilizes both network interactions and gradient features from EEG data.
  • Future work can explore more specific features from inter-channel EEG information exchange to further boost accuracy.