In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 2
356-360

Mental arithmetic task classification based on topological representation of EEG-based functional connectivity

Wu Xuankun1,2
Yan Yan2
Jia Zhenhua1
Bai Xueli1
Wang Lei2
1. School of Computer Science, North China Institute of Aerospace Engineering, Langfang Hebei 065000, China
2. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen Guangdong 518055, China
DOI: 10.19734/j.issn.1001-3695.2021.06.0267

Abstract

Using the method of brain network graph to analyze EEG functional connectivity has the problems of threshold selection and ignoring brain dynamic. To solve this problem, This paper proposed a method of using topological dynamic mode-ling to analyze the EEG functional connectivity matrix, which improved the accuracy of classification and recognition of mental arithmetic tasks. Firstly, it mapped the functional connectivity matrix to an undirected weighted graph. Then it used the persistent Homology Toolbox to construct different complexes and record the different levels of homology features formed in this topological dynamic process to form the persistence diagrams. Finally, it calculated the persistence landscape features as the input feature of the random forest classifier for mental state recognition. In the two task of mental arithmetic state recognition and mental arithmetic quality classification, the proposed algorithm obtained the highest recognition accuracy at 99.26% and 99.20%, sensitivity at 97.87% and 99.80% and specificity at 99.78% and 97.64%, respectively, and accuracy at 66.81% and 66.85% in the cross-individual verification experiment. Experimental results show that the proposed algorithm is fully considered all possible thresholds and effectively extracts the classification information of EEG functional connectivity to implement the automatic recognition of EEG mental arithmetic state.

Foundation Support

廊坊市科技局资助项目(2018011051)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.06.0267
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 2
Section: Algorithm Research & Explore
Pages: 356-360
Serial Number: 1001-3695(2022)02-005-0356-05

Publish History

[2021-09-18] Accepted Paper
[2022-02-05] Printed Article

Cite This Article

吴选昆, 颜延, 贾振华, 等. 基于脑电功能连接拓扑表征的心算任务分类 [J]. 计算机应用研究, 2022, 39 (2): 356-360. (Wu Xuankun, Yan Yan, Jia Zhenhua, et al. Mental arithmetic task classification based on topological representation of EEG-based functional connectivity [J]. Application Research of Computers, 2022, 39 (2): 356-360. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)