Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 8
2340-2346,2351

Cross-domain feature learning method for motor imagery EEG signals

Wei Hongyua
Chen Lifeia,b,c
Luo Tianjiana,b
a. College of Computer & Cyber Security, b. Digital Fujian Environmental Monitoring Internet of Things Laboratory, c. Fujian Applied Mathematics Center, Fujian Normal University, Fuzhou 350108, China
DOI: 10.19734/j.issn.1001-3695.2022.01.0016

Abstract

Motor imagery EEG signals requires a high cost for recording, and there is a large difference for individual's signals. Cross-subject motor imagery EEG signals recognition task belongs to a typical cross-domain learning problem with a small samples set. To solve this problem, this paper proposed a cross-domain feature learning method for motor imagery EEG signals to improve the recognition performance. The proposed method firstly selected the optimal measurement to align the covariance of EEG signals, and then extracted common spatial patterns(CSP) from the aligned EEG trials. Secondly, based on the CSP features, it selected an optimal domain adaptation algorithm to learn the optimal cross-domain features for the target domain. To validate the feasibility and effectiveness of the learned cross-domain features, it adopted a classical model to recognize the learned cross-domain features, and conducted the comparative experiments on two public datasets. Experimental results show that the learned cross-domain features are obviously better than state-of-the-arts methods in recognition performance. In addition, this paper also compared the parameters setting, performance and efficiency for the proposed method.

Foundation Support

国家自然科学基金资助项目(U1805263)
国家自然科学基金青年项目(62106049)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.01.0016
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 8
Section: Algorithm Research & Explore
Pages: 2340-2346,2351
Serial Number: 1001-3695(2022)08-017-2340-07

Publish History

[2022-03-25] Accepted Paper
[2022-08-05] Printed Article

Cite This Article

韦泓妤, 陈黎飞, 罗天健. 运动想象脑电信号的跨域特征学习方法 [J]. 计算机应用研究, 2022, 39 (8): 2340-2346,2351. (Wei Hongyu, Chen Lifei, Luo Tianjian. Cross-domain feature learning method for motor imagery EEG signals [J]. Application Research of Computers, 2022, 39 (8): 2340-2346,2351. )

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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