A Generalized Subspace Approach for Estimating Visual Evoked Potentials

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 P100'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).