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.
['Mohammad Reza Haji Samadi', 'Zohreh Zakeri', 'Neil Cooke']
VOG-enhanced ICA for removing blink and eye-movement artefacts from EEG