Recurrent convolutional neural network model based on temporal and spatial feature for motor imagery classification

Seung Bo Lee, Hakseung Kim, Ji Hoon Jeong, In Nea Wang, Seong Whan Lee, Dong-Joo Kim

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

2 Citations (Scopus)

Abstract

Brain computer interface (BCI) could be useful in improving the quality of life for paralyzed patients. Motor imagery classification has recently been a center of research interest in the BCI-based rehabilitation. As of current, spatial features and spectral features were often used independently for motor imagery classification. While few studies attempted to combine the information from varying domains including spectral, spatial and temporal feature, the attempts employed simplistic linear models. In this study, a novel feature extraction method for including spatial and temporal information is proposed. The method uses recurrent convolutional neural network (RCNN) which excels in temporal and spatial classification. The method was tested for classifying wrist twisting-related task classification during manipulation of robotic arm via electroencephalography, and the performance of the method was compared to the conventional motor imagery classifiers with common spatial pattern (CSP) filter. The proposed method showed 73.9% accuracy in the classification of three types of tasks, whereas the highest accuracy achieved by conventional models was 59.5%. Overall, the performance of the proposed RCNN model was greater than the conventional models using the CSP as input features. The findings warrant further application of the proposed methods in varying BCI environment.

Original languageEnglish
Title of host publication7th International Winter Conference on Brain-Computer Interface, BCI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538681169
DOIs
Publication statusPublished - 2019 Feb 1
Event7th International Winter Conference on Brain-Computer Interface, BCI 2019 - Gangwon, Korea, Republic of
Duration: 2019 Feb 182019 Feb 20

Publication series

Name7th International Winter Conference on Brain-Computer Interface, BCI 2019

Conference

Conference7th International Winter Conference on Brain-Computer Interface, BCI 2019
CountryKorea, Republic of
CityGangwon
Period19/2/1819/2/20

Keywords

  • deep learning
  • motor imagery
  • recurrent convolutional neural network
  • robot arm

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Signal Processing
  • Neuroscience (miscellaneous)

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    Lee, S. B., Kim, H., Jeong, J. H., Wang, I. N., Lee, S. W., & Kim, D-J. (2019). Recurrent convolutional neural network model based on temporal and spatial feature for motor imagery classification. In 7th International Winter Conference on Brain-Computer Interface, BCI 2019 [8737350] (7th International Winter Conference on Brain-Computer Interface, BCI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2019.8737350