Local estimate of surface Laplacian derivation on a realistically shaped scalp surface and its performance on noisy data.
paper
Cited
Public
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- Authors
- Le, J; Menon, V; Gevins, A
- Year
- 1994
- Journal
- Electroencephalography and clinical neurophysiology
- PMID
- 7523088
- DOI
- 10.1016/0168-5597(94)90021-3
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.
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