Applications of EEG neuroimaging data: event-related potentials, spectral power, and multiscale entropy.

摘要: When considering human neuroimaging data, an appreciation of signal variability represents a fundamental innovation in the way we think about brain signal. Typically, researchers represent brain's response as mean across repeated experimental trials and disregard fluctuations over time "noise". However, it is becoming clear that conveys meaningful functional information neural network dynamics. This article describes novel method multiscale entropy (MSE) for quantifying variability. MSE may be particularly informative dynamics because shows timescale dependence sensitivity to linear nonlinear data.