Time Series Analysis of Simulated fMRI Signals - 2010
A wavelet decomposition allows us to decompose a set of signals into different detail levels. Each detail level encompasses a unique range of amplitudes and frequencies of the original signal. This type of decomposition is may provide considerable insight when applied in the analysis of neural signals which are composed of numerous overlapping and most likely interacting signals.
This paper deals with a simulated data set: a set of 47 time series. The simulation is intended to model the BOLD signal obtained from an fMRI. It is suspected that this simulation might exhibit similar overlapping signals to a real fMRI signal. Although a set of real signals do exist, the data set is very large, and will not be analyzed here.
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