A Hybrid Transfer Learning Approach for Motor Imagery Classification in Brain-Computer Interface

Xuying Wang, Rui Yang, Mengjie Huang*, Zhengni Yang, Zitong Wan

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

8 Citations (Scopus)

Abstract

The classification of motor imagery (MI) signal is a representative problem in brain-computer interface (BCI) systems. Because one main application field of MI-based BCI is medical rehabilitation, it is often difficult to obtain a large amount of labeled data from the same subject. Moreover, there are huge individual differences among subjects, so the data from other subjects can not be directly used to train the classifier of the target subject. A transfer learning approach which based on data alignment and deep transfer learning is proposed to solve above problem, and the effectiveness of the proposed approach is verified by experiments based on open dataset.

Original languageEnglish
Title of host publicationLifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages496-500
Number of pages5
ISBN (Electronic)9781665418751
DOIs
Publication statusPublished - 9 Mar 2021
Event3rd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2021 - Nara, Japan
Duration: 9 Mar 202111 Mar 2021

Publication series

NameLifeTech 2021 - 2021 IEEE 3rd Global Conference on Life Sciences and Technologies

Conference

Conference3rd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2021
Country/TerritoryJapan
CityNara
Period9/03/2111/03/21

Keywords

  • Brain-computer interface
  • Convolutional neural network
  • Motor imagery
  • Transfer learning

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