Title
Power spectra constrained IVA for enhanced detection of SSVEP content
Abstract
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).
Year
DOI
Venue
2017
10.1109/CISS.2017.7926104
2017 51st Annual Conference on Information Sciences and Systems (CISS)
Keywords
Field
DocType
Constrained Independent Vector Analysis,Steady State Visual Evoked Potentials
Computer science,A priori and a posteriori,Brain–computer interface,Speech recognition,Spectral density,Time–frequency analysis,Frequency analysis,Blind signal separation,Visual perception,Electroencephalography
Conference
ISBN
Citations 
PageRank 
978-1-5090-2697-5
0
0.34
References 
Authors
10
6
Name
Order
Citations
PageRank
Darren Emge1364.27
Zois Boukouvalas2106.27
Yuri Levin-Schwartz3255.21
Suchita Bhinge433.80
Qunfang Long503.38
Tülay Adali61690126.40