Beamforming and blind source separation have a complementary effect in reducing tonic cranial muscle contamination of scalp measurements

Azin S. Janani, Hanieh Bakhshayesh, Tyler S. Grummett, John O. Willoughby, Kenneth J. Pope

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

1 Citation (Scopus)

Abstract

Scalp electroencephalograms (EEG) are susceptible to cranial and cervical muscle contamination from frequencies as low as 20 hertz, even in relaxed conditions. Reliably recording cognitive activity, which is in this range, is impossible without removing or reducing the effect of muscle contamination. Our unique database of paralysed conscious subjects enabled us to test the effect of combining beamforming and blind source separation in reducing tonic muscle contamination of scalp electrical recordings. Using the beamforming technique, muscle sources are separated automatically based on their location; while using blind source separation, muscle components are separated based on their spectral gradient. Our results show that applying the beamforming technique on data pruned by a blind source separation technique (or vice versa) can reduce tonic muscle contamination significantly more than applying either of them separately, especially at peripheral locations. Hence, these approaches complement each other in reducing muscle contamination of EEG.

Original languageEnglish
Title of host publication2018 26th European Signal Processing Conference, EUSIPCO 2018
Place of PublicationRome, Italy
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages86-90
Number of pages5
ISBN (Electronic)9789082797015
DOIs
Publication statusPublished - 29 Nov 2018
Event26th European Signal Processing Conference, EUSIPCO 2018 - Rome, Italy
Duration: 3 Sep 20187 Sep 2018

Publication series

NameEuropean Signal Processing Conference
Volume2018-September
ISSN (Print)2219-5491

Conference

Conference26th European Signal Processing Conference, EUSIPCO 2018
CountryItaly
CityRome
Period3/09/187/09/18

Keywords

  • Beamforming
  • Blind source separation
  • Electroencephalograph
  • Muscle contamination
  • Neurophysiological response

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