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Chunk #18 — 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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It is also intuitively obvious from Fig. 4 that subtracting the value measured at location E from all other sites (the equivalent of rereferencing the data series to site E) will merely shift the original data series without changing its shape. Therefore, its first and second derivative will remain the same. This further indicates that the sign (or polarity) of the second derivative is not affected by the subtraction of a constant (or referencing the data), in contrast to the original data. Although less obvious, Figure 4 also reveals another characteristic of this data transformation: larger changes in amplitude at neighboring sites are enhanced when compared to the original data, which has been described as a signal ‘edge detection’ feature.4