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Wavelet methods for time series analysis Andrew T. Walden, Donald B. Percival
Publisher: Cambridge University Press

It should be remarked that the definition of functional connections in previous FW analysis methods [4], [6]–[11] is basically based on the Pearson's correlation approach (two signals are correlated if we can predict the variations of one as a function of the other). Random number generation; Calculations on statistical data; Correlation and regression analysis; Multivariate methods; Analysis of variance and contingency table analysis; Time series analysis; Nonparametric statistics. If the value of In this paper, we develop a method to construct a new type of FW from regional fMRI time series, in which PS degree [24], [25] between two regional fMRI time series is taken as the functional connection strength. Time series analysis covers methods attempting to understand context of series or to make forecasts. Wavelet Transform Coherence (WTC) analysis overcomes the problem of non-stationarity by providing a time-frequency analysis of the coherence between two time-series x and y [42,50]. Wavelet analysis was performed to examine the foveation characteristics, morphologic characteristics and time variation in different INS waveforms. Data were analyzed from accurate eye-movement recordings of INS patients. We analyzed electroencephalography (EEG) data from 15 participants with ASC and 15 typical controls, using Wavelet Transform Coherence (WTC) to calculate interhemispheric coherence during face and chair matching tasks, for EEG frequencies from 5 to 40 Hz and during the first .. A growing exploration of patterns of The wavelet analysis technique not only determines the frequency components of the input signal but also their locations in time [38,39]. Variability analysis is essentially a collection of various mathematical and computational techniques that characterize biologic time series with respect to their overall fluctuation, spectral composition, scale-free variation, and degree of irregularity or complexity.