Single-trial EEG emotion recognition using Granger Causality/Transfer Entropy analysis

Yunyuan Gao; Xiangkun Wang; Thomas Potter; Jianhai Zhang; Yingchun Zhang

doi:10.1016/j.jneumeth.2020.108904

Single-trial EEG emotion recognition using Granger Causality/Transfer Entropy analysis

J Neurosci Methods. 2020 Dec 1:346:108904. doi: 10.1016/j.jneumeth.2020.108904. Epub 2020 Sep 6.

Authors

Yunyuan Gao¹, Xiangkun Wang², Thomas Potter³, Jianhai Zhang⁴, Yingchun Zhang⁵

Affiliations

¹ Intelligent Control and Robotics Institute, College of Automation, Hangzhou Dianzi University, Hangzhou, China; Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China. Electronic address: gyy@hdu.edu.cn.
² Intelligent Control and Robotics Institute, College of Automation, Hangzhou Dianzi University, Hangzhou, China.
³ Department of Biomedical Engineering, University of Houston, Houston, USA.
⁴ Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China.
⁵ Department of Biomedical Engineering, University of Houston, Houston, USA. Electronic address: yzhang94@uh.edu.

PMID: 32898573
DOI: 10.1016/j.jneumeth.2020.108904

Abstract

Background: Emotion recognition has been studied for decades, but the classification accuracy needs to be improved.

New method: In this study, a novel emotional classification approach is proposed by combining the Histogram of Oriented Gradient (HOG) method with the Granger Causality (GC) or Transfer Entropy (TE) methods. HOG extracts local valid information from the GC/TE relationship matrices and then the Support Vector Machine (SVM) is employed to classify the emotional states of stress and calm.

Results: Compared with previous studies, the classification accuracy has been greatly improved. The results of this study show that the classification based on GC or TE with HOG offers an average accuracy 88.93 % and 95.21 %, respectively. The achieved accuracy is about 12 % higher than that achieved without using HOG feature extraction.

Comparison with existing method(s): Numerous efforts have been devoted to classify emotional states by extracting EEG characteristics on a single channel basis, the method developed in this study utilizes information interaction between brain channels as a feature to classify emotional states. Furthermore, this study combines HOG and relation matrices for the first time and uses image processing to extract EEG features.

Conclusion: Our results demonstrate the feasibility of combining TE with HOG for emotion recognition with improved classification accuracy by taking advantage of both network and gradient features. More specific features can be selected to improve classification accuracy by taking advantage of information exchanges between EEG channels directly or the extracted property of the relationship matrix based on information interactions.

Keywords: Electroencephalogram; Emotion recognition; Granger Causality; Histogram of oriented gradient; Transfer Entropy.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Electroencephalography*
Emotions
Entropy
Image Processing, Computer-Assisted
Support Vector Machine*