Motor Imagery Classification Based on CNN-GRU Network with Spatio-Temporal Feature Representation

Ji Seon Bang, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Recently, various deep neural networks have been applied to classify electroencephalogram (EEG) signal. EEG is a brain signal that can be acquired in a non-invasive way and has a high temporal resolution. It can be used to decode the intention of users. As the EEG signal has a high dimension of feature space, appropriate feature extraction methods are needed to improve classification performance. In this study, we obtained spatio-temporal feature representation and classified them with the combined convolutional neural networks (CNN)-gated recurrent unit (GRU) model. To this end, we obtained covariance matrices in each different temporal band and then concatenated them on the temporal axis to obtain a final spatio-temporal feature representation. In the classification model, CNN is responsible for spatial feature extraction and GRU is responsible for temporal feature extraction. Classification performance was improved by distinguishing spatial data processing and temporal data processing. The average accuracy of the proposed model was 77.70% (±15.39) for the BCI competition IV_2a data set. The proposed method outperformed all other methods compared as a baseline method.

Original languageEnglish
Title of host publicationPattern Recognition - 6th Asian Conference, ACPR 2021, Revised Selected Papers
EditorsChristian Wallraven, Qingshan Liu, Hajime Nagahara
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783031023743
Publication statusPublished - 2022
Event6th Asian Conference on Pattern Recognition, ACPR 2021 - Virtual, Online
Duration: 2021 Nov 92021 Nov 12

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13188 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference6th Asian Conference on Pattern Recognition, ACPR 2021
CityVirtual, Online

Bibliographical note

Funding Information:
Keywords: Brain-computer interface (BCI) · Electroencephalography (EEG) · Motor imagery (MI) · Convolutional neural network (CNN) · Gated recurrent unit (GRU) This work was partly supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2015-0-00185, Development of Intelligent Pattern Recognition Softwares for Ambulatory Brain Computer Interface, No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning, No. 2019-0-00079, Artificial Intelligence Graduate School Program (Korea University)).

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.


  • Brain-computer interface (BCI)
  • Convolutional neural network (CNN)
  • Electroencephalography (EEG)
  • Gated recurrent unit (GRU)
  • Motor imagery (MI)

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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