Classifying directions in continuous arm movement from EEG signals

Jeong Seok Woo, Klaus Muller, Seong Whan Lee

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

12 Citations (Scopus)

Abstract

EEG based upper limb rehabilitation has limitation on the control commands of neuro-prosthetics cannot deal with human's real movements. To resolve this problem, it is important to know about neural correlation of the directions of arm movement. Previous studies classified the directions of arm movement, using center-out task, only including y-z-axis movement. In this research, 4 subjects participated in experiment and the movement of their right arm in infinity shape (∞) divided into six part of symbol. Moreover, we used Common Spatial Pattern (CSP) algorithm to extract finer feature of EEG signal and Linear Discriminant Analysis (LDA) method to classify directions of movement. The result states that, average of classification accuracy was 74% and standard derivation was 0.08. In the topographical map at the center of infinity shape, we could observe the divided image of left and right side of the brain and FC3, F7 and C3 channels included most information about directions of movement. By the result of this study, we can confirm the possibility of controlling neuro-prosthetics and evidence of neurological basis of the arm movement.

Original languageEnglish
Title of host publication3rd International Winter Conference on Brain-Computer Interface, BCI 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781479974948
DOIs
Publication statusPublished - 2015 Mar 30
Event2015 3rd International Winter Conference on Brain-Computer Interface, BCI 2015 - Gangwon-Do, Korea, Republic of
Duration: 2015 Jan 122015 Jan 14

Other

Other2015 3rd International Winter Conference on Brain-Computer Interface, BCI 2015
CountryKorea, Republic of
CityGangwon-Do
Period15/1/1215/1/14

Keywords

  • Arm movement direction
  • BCI
  • Common spatial pattern
  • EEG
  • Upper limb rehabilitation

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Cognitive Neuroscience
  • Sensory Systems

Fingerprint Dive into the research topics of 'Classifying directions in continuous arm movement from EEG signals'. Together they form a unique fingerprint.

  • Cite this

    Woo, J. S., Muller, K., & Lee, S. W. (2015). Classifying directions in continuous arm movement from EEG signals. In 3rd International Winter Conference on Brain-Computer Interface, BCI 2015 [7073054] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2015.7073054