TY  - CONF
ID  - scholars500
SP  - 5208
TI  - A generalized subspace approach for estimating visual evoked potentials
KW  - Covariance matrix; Eigenvalues and eigenfunctions; Electroencephalography; Electrophysiology; Higher order statistics
KW  -  Covariance matrices; Electro-encephalogram (EEG); Generalized eigen decomposition; Generalized eigenvalue decomposition; Objective assessment; Subspace approach; Subspace filtering; Visual evoked potential
KW  -  Biomedical signal processing
KW  -  algorithm; article; computer assisted diagnosis; electroencephalography; event related potential; evoked visual response; human; methodology; physiology; reproducibility; sensitivity and specificity; signal processing; visual cortex
KW  -  Algorithms; Diagnosis
KW  -  Computer-Assisted; Electroencephalography; Event-Related Potentials
KW  -  P300; Evoked Potentials
KW  -  Visual; Humans; Reproducibility of Results; Sensitivity and Specificity; Signal Processing
KW  -  Computer-Assisted; Visual Cortex
N1  - 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
N2  - 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.
AV  - none
CY  - Vancouver, BC
EP  - 5211
A1  - Kamel, N.
A1  - Yusoff, M.Z.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-61849097776&doi=10.1109%2fiembs.2008.4650388&partnerID=40&md5=d1cc0a98e1e593a9efc7f202147c9fcb
PB  - IEEE Computer Society
SN  - 9781424418152
Y1  - 2008///
ER  -