Constrained and seeded independent vector analysis for steady state visually evoked potential processing and detection

Tulay Adali, CSEE
Zois Boukouvalas, Mathematics & Statistics
Rami Mowakeaa, CSEE
Darren Emge, CSEE

The detection of steady state visual evoked potentials (SSVEPs) has been identified as an effective solution for brain computer interface (BCI) systems as well as for neurocognitive investigations of visually related tasks. SSVEPs are induced at the same frequency as the visual stimuli being presented and can be observed in the scalp-based recordings of electroencephalogram signals, though they are one component buried amongst the normal brain signals and complex noise. Variations in individual hemodynamic responses as well as the presence of multiple biological artifacts complicate the use of direct frequency analysis making blind source separation methods, e.g., independent component (ICA) and independent vector analysis (IVA), desirable solutions for these applications. IVA has been shown capable of enhancing and improving detection of SSVEPs by exploiting the complimentary information that exists across EEG channels. In this work, we present a novel implementation of IVA called seeded IVA, which incorporates a priori information to seed or bias the source estimation, thus improving convergence and reducing the computational time associated with standard IVA implementations