Hybrid Zero-Training BCI based on Convolutional Neural Network for Lower-limb Motor-Imagery

Ji Hyeok Jeong, Dong Joo Kim, Hyungmin Kim

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

Abstract

Zero-training BCI was presented to overcome the inconvenience and impractical aspects of the training session in the Brain-Computer Interface (BCI) based on Motor Imagery (MI). Zero-training BCI can be classified into a session-to-session transfer BCI and a subject-independent BCI. The session-to-session transfer BCI is characterized by high classification accuracy, but there is a limitation that the model could not be improved as the number of subjects increased. On the other hand, the subject-independent BCI has advantage in increasing the number of subjects, but had the problem of requiring too many subjects for high accuracy. In this study, we proposed the hybrid zero-training BCI that integrates the advantages of the aforementioned two methods and Multidomain CNN that combined time-, spatial-, and phase-domain, and aimed for more practical application and higher classification accuracy. We collected three-class MI EEG data related to lower-limb movement (gait, sit-down, and rest) from three subjects with three sessions per subject. The classification accuracy of the proposed method (82.10 pm 10.66%) in the classification of three-class of MI tasks was significantly higher than that of the existing zero-training BCIs (66.42 ± 9.68\%, 66.67±6.83\%) I, and also higher than the conventional BCI (70.86±9.46\%) that trains and evaluates with training sessions collected on the same day although not statistically significant.

Original languageEnglish
Title of host publication9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728184852
DOIs
Publication statusPublished - 2021 Feb 22
Event9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021 - Gangwon, Korea, Republic of
Duration: 2021 Feb 222021 Feb 24

Publication series

Name9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021

Conference

Conference9th IEEE International Winter Conference on Brain-Computer Interface, BCI 2021
Country/TerritoryKorea, Republic of
CityGangwon
Period21/2/2221/2/24

Keywords

  • Brain-Computer interface
  • Convolutional neural network
  • EEG
  • Motor imagery
  • Zero-training

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Signal Processing

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