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Algorithm Research & Explore
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1728-1733

Finger motor imagery based on optimal sub-segment deep learning research on EEG signal classification

Zhou Peng
Ye Qingwei
Luo Huiyan
Lu Zhihua
Faculty of Electrical Engineering & Computer Science, Ningbo University, Ningbo Zhejiang 315211, China

Abstract

The classification performance of the existing multi-classification tasks of finger motor imagery EEG signals is difficult to achieve usable performance. On the basis of detailed analysis of multiple components on the time scale of EEG signals, this paper designed a self-supervised sub-network for signal sub-segment extraction, and then input the sub-segment into the next sub-network for signal classification, it synthesized the two sub-networks into a self-supervised hybrid multi-task deep network. In the training phase, the sub-segment extraction sub-network extracted different sub-segments for each EEG signal, and the subsequent classification sub-network judged whether the sub-segment was the best and automatically adjusted the position of the sub-segment. It weighted the total loss function by two loss functions of two sub-networks, and extracted the best sub-segment signal and obtained the best classification effect through the overall network learning algorithm. In the verification and testing phase, the sub-segment extraction sub-network automatically extracted the corresponding sub-segment input classification sub-network according to the parameters of the training for classification. It verified the network performance on the largest SCP data of Motor-Imagery data set and the Data sets 4 of BCI Competition IV. On the SCP dataset, the average test classification accuracy of all subjects' three finger classification tasks is more than 70%, the average test classification accuracy of four finger classification tasks is about 60%, and the average test classification accuracy of five finger classification tasks is about 50%, which is significantly improved compared with the existing reports. It is confirmed that the network can effectively extract sub-segments of motor imagery EEG signals, and has good classification effect and generalization performance.

Foundation Support

国家自然科学基金资助项目(61071198,51675286)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.11.0553
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 6
Section: Algorithm Research & Explore
Pages: 1728-1733
Serial Number: 1001-3695(2023)06-020-1728-06

Publish History

[2023-01-17] Accepted Paper
[2023-06-05] Printed Article

Cite This Article

周鹏, 叶庆卫, 罗慧艳, 等. 基于最优子段深度学习的手指运动想象脑电信号分类研究 [J]. 计算机应用研究, 2023, 40 (6): 1728-1733. (Zhou Peng, Ye Qingwei, Luo Huiyan, et al. Finger motor imagery based on optimal sub-segment deep learning research on EEG signal classification [J]. Application Research of Computers, 2023, 40 (6): 1728-1733. )

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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