Beamformer Source Estimation Improves Accuracy of Motor-Imagery-based Brain Computer Interface

Hui Ling Chan, Jung Wei Wang, Yong-Sheng Chen

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

Abstract

Motor-imagery-based brain computer interface has advantages in high information transition rate and has potential to achieve high portability without using a display for visual stimulation. However, electroencephalographic data is a mixture of neural activity from various brain locations and also external artifacts. This paper presents a novel method utilizing source-level features computed based on maximum contrast beamformer. The estimated source activity has the maximum contrast between the power during the reference period and the period that event-related synchronization or desynchronization emerges. Moreover, this method utilizes the classification structure based on divide-and-conquer concept to identify four classes of motor imageries, including left hand, right hand, tongue, and foot. The experimental results demonstrate the improvement in classification accuracy by using the proposed method.

Original languageEnglish
Title of host publication2018 IEEE 23rd International Conference on Digital Signal Processing, DSP 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538668115
DOIs
StatePublished - 31 Jan 2019
Event23rd IEEE International Conference on Digital Signal Processing, DSP 2018 - Shanghai, China
Duration: 19 Nov 201821 Nov 2018

Publication series

NameInternational Conference on Digital Signal Processing, DSP
Volume2018-November

Conference

Conference23rd IEEE International Conference on Digital Signal Processing, DSP 2018
CountryChina
CityShanghai
Period19/11/1821/11/18

Keywords

  • beamformer source estimation
  • brain computer interface
  • divide-and-conquer classification
  • electroencephalography
  • event-related desyncrhonization
  • motor imagery

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