Phase Transition in previous Motor Imagery affects Efficiency of Motor Imagery based Brain-computer Interface

Min Kyung Jung, Seho Lee, In Nea Wang, Ha Yoon Song, Hakseung Kim, Dong Joo Kim

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

Abstract

A electroencephalography (EEG) based braincomputer interface (BCI) provides a communication channel to operate the external environment by decoding brain patterns. Movement-Related Cortical Potentials (MRCPs) are one particular type of EEG pattern during movement of peripheral limbs. The performance of motor imagery (MI) is related to pattern of MRCPs by planning simulation. In resent decade, MI-based BCI have shown potential as its performance significantly improved. In this study, the feasibility of selected-based method was proposed by compared with conventional method. The detection accuracy overall performances were 72.42± 3.12\%. When a top 97.8% trial is selected, overall performance improved approximately 3.15% compared to baseline. When MI were analyzed in non-selected trials, C3 and C4 channel showed no different aspects in left and right class respectively. The brain state was changed after the cue appeared, and these power of delta band appeared in all subjects. The performance of classification was improved by rejecting trials with no difference between the state before and after cue.

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
  • electroencephalography
  • motor imagery
  • movement-related cortical potentials

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Signal Processing

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