VOG-enhanced ICA for SSVEP response detection from consumer-grade EEG

The steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) paradigm detects when users look at flashing static and dynamic visual stimuli. Electroculogram (EOG) artefacts in the electroencephalography (EEG) signal limit the application for dynamic stimuli because they elicit smooth pursuit eye movement. We propose `VOG-ICA´ - an EOG artefact rejection technique based on Independent Component Analysis (ICA) that uses video-oculography (VOG) information from an eye tracker. It demonstrates good performance compared to Plöchl when evaluated on matched and EEG data collected with consumer grade eye tracking and wireless cap EEG apparatus. SSVEP response detection from frequential features extracted from ICA components demonstrates higher SSVEP response detection accuracy and lower between-person variation compared with extracted features from raw and post-ICA reconstructed `clean´ EEG. The work highlights the requirement for robust EEG artefact and SSVEP response detection techniques for consumer-grade multimodal apparatus.