%P 5208-5211
%A N. Kamel
%A M.Z. Yusoff
%I IEEE Computer Society
%T A generalized subspace approach for estimating visual evoked potentials
%C Vancouver, BC
%J Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"
%L scholars500
%O cited By 5; Conference of 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 ; Conference Date: 20 August 2008 Through 25 August 2008; Conference Code:75336
%R 10.1109/iembs.2008.4650388
%D 2008
%X A "single-trial" signal subspace approach for extracting visual evoked potential (VEP) from the ongoing "colored" electroencephalogram (EEG) noise is proposed. The algorithm applies the generalized eigendecomposition on the covariance matrices of the VEP and noise to transform them jointly into diagonal matrices in order to avoid a prewhitening stage. The proposed generalized subspace approach (GSA) decomposes the corrupted VEP space into a signal subspace and noise subspace. Enhancement is achieved by removing the noise subspace and estimating the clean VEPs only from the signal subspace. The validity and effectiveness of the proposed GSA scheme in estimating the latencies of PlOO's (used in objective assessment of visual pathways) are evaluated using real data collected from Selayang Hospital in Kuala Lumpur. The performance of GSA is compared with the recently proposed single-trial technique called the Third Order Correlation (TOC). © 2008 IEEE.
%K Covariance matrix; Eigenvalues and eigenfunctions; Electroencephalography; Electrophysiology; Higher order statistics, Covariance matrices; Electro-encephalogram (EEG); Generalized eigen decomposition; Generalized eigenvalue decomposition; Objective assessment; Subspace approach; Subspace filtering; Visual evoked potential, Biomedical signal processing, algorithm; article; computer assisted diagnosis; electroencephalography; event related potential; evoked visual response; human; methodology; physiology; reproducibility; sensitivity and specificity; signal processing; visual cortex, Algorithms; Diagnosis, Computer-Assisted; Electroencephalography; Event-Related Potentials, P300; Evoked Potentials, Visual; Humans; Reproducibility of Results; Sensitivity and Specificity; Signal Processing, Computer-Assisted; Visual Cortex