| Summary: | Estimating a visual evoked potential (VEP) from the human
brain is challenging since its signal-to-noise ratio (SNR) is generally very low. An eigendecomposition-based
subspace approach originally proposed for enhancing
speech corrupted by colored noise, has been investigated
and tested in the single trial extraction of VEPs. This
scheme arbitrarily labeled as an eigen-decomposition (ED)
method has been compared with a third-order correlation
(TOC) method, using both realistic simulation and real
human data. The results produced by the ED algorithm
show much cleaner waveforms, and higher degree of
consistency in detecting the P100, P200, and P300 peaks.
|
|---|