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

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 wavelet transform is almost always implemented as a bank of filters that decompose a signal into multiple signal bands. Wavelets are a relatively new signal processing method. 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]. Although it is not uncommon for users to log data, extract it from a file or database and then analyze it offline to modify the process, many times the changes need to happen during run time. Topic: Functional time series analysis, prediction and classification using BAGIDIS. Venue: Statistics Building (c/o Victoria- and Bosman streets, Stellenbosch), Room 2021. Walden “Wavelet Methods for Time Series Analysis" Cambridge University Press | 2000-07-24 | ISBN: 0521640687 | 620 pages | DJVU | 16 MB. It separates and retains the signal features in one or a few of these subbands. Enquiries: Danie Uys, Tel: 021 808 The method is centered on the definition of a functional, data-driven and highly adaptive semimetric for measuring dissimilarities between curves, typically time series or spectra.