Using simulated expanding and rotating waves, we verified the robustness of our detection approach under Gaussian white noise of varying amplitude. (A) Snapshots of simulated expanding waves in one oscillation cycle (at 85, 105, and 125 ms), under varying levels of noise (standard deviation 0, 0.1, and 0.2). (B) Simulated rotating waves, as in (A). (C) The performance of the algorithm in detecting expanding and rotational waves (blue and red solid lines, mean ± SEM), as compared to the thresholds derived from the permutation test (dotted lines). Every point represents 25 simulated spindles with 14 oscillation cycles each. Even when the noise standard deviation is unity (at parity with oscillation amplitude, highly obscuring visual detection), the algorithm recovers a correlation magnitude around 0.8 in each case, far above the determined permutation thresholds. In contrast, with only Gaussian white noise as input, the algorithm returns a low correlation value for rotational detection, below the determined permutation threshold (gray line). (D) Robustness of the algorithm to center position. This panel illustrates the performance of the algorithm for expanding (blue) and rotating (red) waves at different points on the electrode array, under Gaussian white noise (standard deviation 0.2). Note that performance drops for rotational detection at the border of the array, but this is small in comparison to the threshold determined from the permutation control (dotted red line).