Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 9
2614-2618

Emotion recognition method based on key causal connection of transfer entropy

Wang Zhongmina,b
Cai Lanlana
Fan Lina,b
a. School of Computer Science & Technology, b. Shaanxi Key Laboratory of Network Data Analysis & Intelligent Processing, Xi'an University of Posts & Telecommunications, Xi'an 710121, China
DOI: 10.19734/j.issn.1001-3695.2020.12.0550

Abstract

The information flow presented by human brain in emotional activities is complex and changeable, so it is crucial to understand the dynamic interaction process of brain regions. However, using raw EEG signals to build emotional networks contains a lot of redundant information that has nothing to do with emotions. To solve this problem, this paper proposed a method to remove emotionally irrelevant network connections without losing key causal information, and verified its effectiveness in the process of emotion recognition. Firstly, this method used the transfer entropy causality analysis method to construct the normalized transfer entropy(NTE) matrix for positive, neutral, and negative emotions, and then subtracted the neutral emotion matrix from the matrix of positive and negative emotions. Finally, it used the simplified matrix to construct an effective brain network and used graph theory to analyze the network connectivity of different emotions. Through the verification on DEAP dataset, it is found that this method can effectively improve the accuracy of emotion recognition.

Foundation Support

国家自然科学基金资助项目(61373116)
陕西省工业领域一般项目(2018GY-013)
陕西省教育厅资助项目(18JK0697)
咸阳市科技局资助项目(2017k01-25-2)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.12.0550
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 9
Section: Algorithm Research & Explore
Pages: 2614-2618
Serial Number: 1001-3695(2021)09-009-2614-05

Publish History

[2021-09-05] Printed Article

Cite This Article

王忠民, 蔡兰兰, 范琳. 基于传递熵关键因果连接的情感识别方法 [J]. 计算机应用研究, 2021, 38 (9): 2614-2618. (Wang Zhongmin, Cai Lanlan, Fan Lin. Emotion recognition method based on key causal connection of transfer entropy [J]. Application Research of Computers, 2021, 38 (9): 2614-2618. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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