Skip to main navigation Skip to search Skip to main content

Efficient implementation of RMVB for eyeblink artifacts removal of EEG via STF-TS modeling

  • Yodchanan Wongsawat

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

1 Citation (Scopus)

Abstract

In this paper, eyeblink artifacts of a multi-channel electroencephalogram (EEG) are extracted and removed by employing a spatial filter designed using the proposed efficient method via the robust minimum variance beamforming (RMVB). Unlike the conventional method where the spatial filter is designed using the correlation and prior knowledge of eyeblink from all channels of EEG, the spatial filter is obtained by simultaneously taking into consideration the local correlation and prior knowledge of each groups of EEG channels. The prior knowledge is obtained by taking into account the space-time-frequency information via the STF-TS model. Simulation results show that, by using less computational complexity, the proposed method can efficiently remove eyeblink artifacts from the multichannel EEG as well as the conventional method.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Robotics and Biomimetics, ROBIO 2008
PublisherIEEE Computer Society
Pages1567-1572
Number of pages6
ISBN (Print)1901725537, 9781424426799
DOIs
Publication statusPublished - 2009
Event1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1999 - Bangkok, Thailand
Duration: 21 Feb 200926 Feb 2009

Publication series

Name2008 IEEE International Conference on Robotics and Biomimetics, ROBIO 2008

Conference

Conference1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1999
Country/TerritoryThailand
CityBangkok
Period21/02/0926/02/09

Keywords

  • Artifact removal
  • Beamforming
  • EEG
  • Eyeblink
  • Spatial filter

Fingerprint

Dive into the research topics of 'Efficient implementation of RMVB for eyeblink artifacts removal of EEG via STF-TS modeling'. Together they form a unique fingerprint.

Cite this