As before, from a pragmatic standpoint, the critical question is how differences in noise levels differentially affect SP and SL measures for real EEG data. After reducing the signal-to-noise ratio by limiting the number of trials to compute an error-related negativity, the surface Laplacian was found to render better results than its surface potential counterparts (Cohen, 2014). We manipulated noise levels by evaluating hemifield-dependent N1 asymmetries with nonparametric permutation tests using different sample sizes (N = 130, 80, 40, 20, or 10), which did not affect the superior performance of CSD compared to ERP or EEG measures, although overall statistical significance progressively declined with smaller sample size across all data transformations (Kayser and Tenke, 2015).
