Sequential Transfer Learning via Segment after Cue Enhances the Motor Imagery-based Brain-Computer Interface

Dong Kyu Kim, Young Tak Kim, Hee Ra Jung, Hakseung Kim, Dong Joo Kim

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

Abstract

Brain-computer interface (BCI) based on electroencephalogram (EEG) is a promising technology, allowing computers to estimate human intentions. Intention recognition tool such as motor imagery (MI) with high reliability is one of the major challenges in the BCI field. Recently, researchers have attempted to use transfer learning for various BCI datasets, but the studies showed low classification accuracy. This study aimed to increase the classification accuracy of the MI through sequential transfer learning for a single dataset. EEG-MI data with 9 subjects from the dataset 2a of BCI competition IV were used. EEGNet was used for MI classification. The pre-trained model was constructed by first learning whether the data were MI or not. The model was then sequentially fine-tuned through transfer learning for four MI tasks (i.e., left hand, right hand, both feet and tongue). The model was able to classify MI with 91.34% accuracy. In the meantime, the baseline model without transfer learning showed an accuracy of 61.62%, whereas the fine-tuned model presented an improved accuracy of 63.82%. Consequently, the sequential transfer learning was able to improve the performance of MI-BCI.

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
  • deep learning
  • electroencephalography
  • motor-imagery
  • transfer learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Signal Processing

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