A Training System for Brain-Computer Interfaces Based on Motor Imagery Selection

Yuki Koizumi, Taro Shibanoki, Toshio Tsuji

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

2 Citations (Scopus)

Abstract

This paper proposes a BCI training system based on motor imagery selection. The system selects discriminable motor imagery tasks based on the partial KL information theory and provides related feedback training. Results from training experiments showed a gradual increase in repeatability for all subjects in selected motor imagery tasks.

Original languageEnglish
Title of host publicationLifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages217-218
Number of pages2
ISBN (Electronic)9781728170633
DOIs
Publication statusPublished - Mar 2020
Externally publishedYes
Event2nd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2020 - Kyoto, Japan
Duration: Mar 10 2020Mar 12 2020

Publication series

NameLifeTech 2020 - 2020 IEEE 2nd Global Conference on Life Sciences and Technologies

Conference

Conference2nd IEEE Global Conference on Life Sciences and Technologies, LifeTech 2020
Country/TerritoryJapan
CityKyoto
Period3/10/203/12/20

Keywords

  • brain-computer interface (BCI)
  • class selection
  • electroencephalogram (EEG)
  • Kullback-Leibler divergence
  • motor imagery
  • training system

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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