Motor Imagery Classification using local region CSP features with high-gamma band

Jinwoo Lee, Wonzoo Chung

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

1 Citation (Scopus)

Abstract

In this paper, we present enhanced MI classification method in electroencephalogram(EEG)-based Brain-Computer Interfaces(BCI) with high-gamma band and local region CSP features. In order to improve the performance of MI classification, the use of the local region CSP feature with high eigenvalue disparity score and the high-gamma band related to advanced cognitive information processing such as reasoning and judgment can provide improved performance compared to existing CSP based methods. As a result of the experiment through BCI competition III-IVa dataset, it is shown that MI classification performance is improved through the proposed method.

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

Bibliographical note

Funding Information:
This work was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00432, Development of non-invasive integrated BCI SW platform to control home appliances and external devices by user’s thought via AR/VR interface) and Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning).

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Brain-computer interface (BCI)
  • Common Spatial Pattern (CSP)
  • High-gamma band
  • Local features
  • electroencephalography (EEG)

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Signal Processing

Fingerprint

Dive into the research topics of 'Motor Imagery Classification using local region CSP features with high-gamma band'. Together they form a unique fingerprint.

Cite this