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Chunk #40 — 1. Introduction — 1.3. Surface Laplacian estimation via spherical splines — Spline regularization

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Issues and considerations for using the scalp surface Laplacian in EEG/ERP research: A tutorial review.
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or smoothing (i.e., greater λ value) can - to a certain degree - mutually compensate to achieve optimal potential estimates. As a consequence, λ-optimized CSDs obtained with different spline orders yield more similar surface Laplacian estimates (between-topography correlations were 0.6872 ≤ r ≤ 0.9970) compared to those obtained with a fixed λ value (cf. Fig. 8A, which employed a default smoothing constant of λ = 0.00001 for all spline orders, yielding 0.3775 ≤ r ≤ 0.9693 for 72 channels, and 0.6054 ≤ r ≤ 0.9714 for 31 channels).