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Wavelet analysis is a powerful signal processing method that represents a signal in terms of wavelets. A wavelet or small wave is a wavelike function, upon scaled and translated, that can be used to decompose a signal into its basic constituent components at different scales. Each scale component can be converted into a frequency range. The resulting wavelet transform measures the time-frequency variations of frequency components in a nonstationary signal. Wavelet analysis is thus a natural choice for the analysis of neural data in which the nonstationarity is abundant.