A Time-Domain Subspace Technique for Estimating Visual Evoked Potential Latencies

Estimating a visual evoked potential (VEP) from the human brain is challenging since its signal-to-noise ratio (SNR) is generally very low. An optimization and eigen-decomposition-based subspace approach has been investigated and tested to estimate the latencies of visual evoked potential (VEP) signals which are highly corrupted by spontaneous electro-encephalogram (EEG) waveforms that can be considered as colored noise. This scheme termed as the generalized subspace approach (GSA) depends on the generalized eigendecomposition of the covariance matrices of the VEP and the colored EEG noise. The subspace algorithm jointly transforms these two correlation matrices into diagonal matrices, which can then be segregated into signal subspace and noise subspace. Enhancement is performed by removing the noise subspace and estimating the clean VEP signal from the remaining signal subspace. Further, GSA has been compared with a third-order correlation (TOC) method, using both realistic simulation and real patient data gathered in a hospital. The simulation results produced by the GSA algorithm show more faithful reproduction of VEP waveforms, and a higher degree of consistencies in detecting the P100, P200, and P300 peaks. Additionally, the results of the real patient data confirm the superiority of GSA over TOC in estimating VEP's P100 latencies, which are used by clinicians to assess the conduction of electrical signals from the subjects' retinas to the visual cortex parts of their brains.