Power spectra constrained IVA for SSVEP detection

Abstract

The detection of steady state visual evoked potentials (SSVEPs), an evoked response to visual stimuli, has been identified as an effective solution for brain computer interface systems and as a probe for neurocognitive investigations of visually related tasks. However, since they recorded as part of the scalp-based electroencephalogram (EEG) signals their detection is challenging as they are buried amongst the normal brain signals. Blind source separation methods, such as independent vector analysis (IVA), have been shown to be capable of enhancing and improving signal detection by exploiting the diversity within individual datasets while simultaneously exploiting the complimentary information across datasets. In general, IVA is highly flexible with a general solution space; however, it is not guaranteed to converge to a meaningful minimum, by incorporating a problem specific constraint we can shrink the solution space insuring a relevant solution. In this work, we present a novel multiset data framework for EEG recordings and apply our constrained power spectra IVA (CP-IVA) to a publicly available SSVEP dataset. We compare the prediction accuracy of CP-IVA with that of an optimized processing stream developed for that dataset, as well as a canonical correlation analysis (CCA) based approach, showing that CP-IVA achieves better average performance and is more robust across the population of subjects with a higher minimum detection rate. More importantly, CP-IVA achieves this performance with minimal pre-processing and without the need to train complex classifiers.