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 language | English |
---|---|
Title of host publication | 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 603-606 |
Number of pages | 4 |
ISBN (Electronic) | 9781509024551 |
ISBN (Print) | 9781509024551 |
DOIs | |
Publication status | Published - 21 Apr 2016 |
Event | 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 - Las Vegas, United States Duration: 24 Feb 2016 → 27 Feb 2016 |
Conference
Conference | 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 |
---|---|
Country/Territory | United States |
City | Las Vegas |
Period | 24/02/16 → 27/02/16 |
ASJC Scopus subject areas
- Health Informatics
- Health Information Management