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Chunk #17 — 1. Introduction — 1.2. What is a surface Laplacian transform?

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Issues and considerations for using the scalp surface Laplacian in EEG/ERP research: A tutorial review.
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by its instantaneous change in amplitude that is equal to the slope of a tangent line at each data point (first derivative = differences between neighboring points = gradient). Repeating this operation on the resulting data series (second derivative = differences of these differences), and inverting these secondary slopes, yields a data series that reflects the rate of change in slope across the observed original values. As shown in Figure 4, despite a nominal maximum of the original values at location E (green line), the change rate in slope was notably larger at locations C and G (red line), because site E has neighboring sites of similar amplitude (sites D and F).