VOG-enhanced ICA for removing blink and eye-movement artefacts from EEG

Mohammad Reza Haji Samadi, Zohreh Zakeri, Neil Cooke

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

2 Citations (Scopus)
292 Downloads (Pure)

Abstract

The steady-state visual evoked potential (SSVEP) is reliable for many paradigms such as clinical neuroscience and brain computer interfaces (BCI), providing high information throughput with minimal between-person variations. However, Electrooculogram (EOG) artefacts in Electroencephalography (EEG) signal limit applications with dynamic SSVEP stimuli due to eye movement and blinks. Independent Component Analysis (ICA) is a successful method for removing EOG artefacts. We propose 'Blink VOG-ICA' (BVOG-ICA) - an enhanced ICA algorithm that uses eye tracker video-oculography (VOG) eye movement and blink detection information. It demonstrates improved performance compared to ICA variants Plochl and our previous VOG-ICA when evaluated on matched VOG and EEG data. SSVEP classification accuracy for the post-ICA clean EEG consequently improves 7% and 4% for static and dynamic SSVEP stimuli respectively, suggesting BVOG-ICA as a potentially reliable automatic EOG artefact removal method for SSVEP paradigms.

Original languageEnglish
Title of host publication3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages603-606
Number of pages4
ISBN (Electronic)9781509024551
ISBN (Print)9781509024551
DOIs
Publication statusPublished - 21 Apr 2016
Event3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 - Las Vegas, United States
Duration: 24 Feb 201627 Feb 2016

Conference

Conference3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
Country/TerritoryUnited States
CityLas Vegas
Period24/02/1627/02/16

ASJC Scopus subject areas

  • Health Informatics
  • Health Information Management

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