Local estimate of surface Laplacian derivation on a realistically shaped scalp surface and its performance on noisy data.
A new implementation of the surface Laplacian derivation (SLD) method is described which reconstructs a realistically shaped, local scalp surface geometry using measured electrode positions, generates a local spectral-interpolated potential distribution function, and estimates the surface Laplacian values through a local planar parametric space using a stable numerical method combining Taylor expansions with the least-squares technique. The implementation is modified for efficient repeated SLD operations on a time series. Examples are shown of applications to evoked potential data. The resolving power of the SLD is examined as a function of the spatial signal-to-noise (SNR) ratio. The analysis suggests that the Laplacian is effective when the spatial SNR is greater than 3. It is shown that spatial low-pass filtering with a Gaussian filter can be used to reduce the effect of noise and recover useful signal if the noise is spatially incoherent.
Le
J
J
EEG Systems Laboratory, San Francisco, CA 94105.
Menon
V
V
Gevins
A
A
D004560:Electricity; D004569:Electroencephalography / Q000379:methods*; D005071:Evoked Potentials; D006801:Humans