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Chunk #37 — 1. Introduction — 1.3. Surface Laplacian estimation via spherical splines — Montage density

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Issues and considerations for using the scalp surface Laplacian in EEG/ERP research: A tutorial review.
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surface points, for m = 2, r = 0.5954; for m = 3, r = 0.8916), whereas less flexible splines largely retain the 72-channel topography (for m = 4, r = 0.9752; for m = 5, r = 0.9754). Thus, spline flexibility acts as a spatial filter that can be optimized to enhance the signal of interests, for example, by choosing a less flexible spline for ERP studies in the time domain aimed at group and/or condition comparisons (e.g., Kayser and Tenke, 2006a, 2006b;Law et al., 1993b), or a more flexible spline for studying individual EEG coherence in the frequency domain (e.g., Nunez et al., 1997, 1999; Srinivasan et al., 1998a).10 However, a low-density EEG montage will itself impose a spatial low pass filter (e.g., Srinivasan et al., 1998b; Tucker, 1993).