Power spectra constrained IVA for enhanced detection of SSVEP content

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 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 response latencies as well as the presence of multiple biological artifacts complicate the use of direct frequency analysis, thus making blind source separation methods, such as independent component (ICA) and independent vector analysis (IVA) desirable solutions. IVA is a recent extension of ICA that decomposes multiple datasets simultaneously and has been been shown to be capable of enhancing and improving the detection of SSVEPs by exploiting the complimentary information that exists across EEG channels. In this work, we present a novel extension of IVA which incorporates a priori information to constrain the power spectral density (PSD) of the source estimates, known as constrained PSD IVA (CP-IVA) and demonstrate its improved SSVEP detection performance as well as stability over standard IVA and temporally constrained IVA (C-IVA).