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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 5
1344-1349

EEG emotion recognition based on common spatial pattern of brain functional network

Liu Ke1a,1b
Zhang Xiao1a
Li Peiyang1c
Chen Duo2
Wang Guoyin1a,1b
1. a. School of Computer Science & Technology, b. Chongqing Key Laboratory of Computational Intelligence, c. School of Bioinformatics, Chongqing University of Posts & Telecommunications, Chongqing 400065, China
2. School of Computer Science & Engineering, Nanyang Technological University, Singapore 639798, Singapore
DOI: 10.19734/j.issn.1001-3695.2020.07.0181

Abstract

The emotion classification of traditional brain network uses clustering coefficients, average shortest path and other topological attributes as classification features. To solve the problem that EEG emotion recognition based on these attributes is susceptible to network connectivity thresholds and attribute selection, and network topology attributes are difficult in fully characterizing the differences in network structure for different emotional states, this paper proposed an EEG emotion classification based on common spatial patterns of brain networks topology(EEC-CSP-BNT). EEC-CSP-BNT calculated the functional connectivity matrix in the sensor space using mutual information for each sub-band, and employed the CSP to learn the spatial filters and constructed classification features. At last, it employed the pattern classifiers(such as Fisher linear discrimination, support vector machine and K nearest neighbor) to complete the emotion recognition. Experimental results using DEAP and SEED datasets validate the superior performance of EEC-CSP-BNT compared to the network topology attributes features. EEC-CSP-BNT can also extract the useful classification information of brain network topology.

Foundation Support

国家自然科学基金资助项目(61703065,61901077,61876201)
重庆市基础研究与前沿探索项目(cstc2018jcyjAX0151)
重庆市教委科学技术研究项目(KJQN201800612)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.07.0181
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 5
Section: Algorithm Research & Explore
Pages: 1344-1349
Serial Number: 1001-3695(2021)05-011-1344-06

Publish History

[2021-05-05] Printed Article

Cite This Article

刘柯, 张孝, 李沛洋, 等. 基于脑功能网络和共空间模式分析的脑电情绪识别 [J]. 计算机应用研究, 2021, 38 (5): 1344-1349. (Liu Ke, Zhang Xiao, Li Peiyang, et al. EEG emotion recognition based on common spatial pattern of brain functional network [J]. Application Research of Computers, 2021, 38 (5): 1344-1349. )

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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