About

BrainWavelet is an initiative from the Brain Mapping Unit at the University of Cambridge for fast and effective time series denoising. The site and toolboxes were set-up and are run by Ameera Patel (contact). The Wavelet Despiking code was originally developed for removal of motion artefacts from resting-state fMRI data (see citation below), but also has applications for denoising other biological signals, such as local field potential signals.

Citation: Patel AX, et al. (2014) A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series. NeuroImage. 95:287-304.   (http://dx.doi.org/10.1016/j.neuroimage.2014.03.012)
PubMed   |   BibTeX

@article{PatelAX2014,
author = {Patel, Ameera X and Kundu, Prantik and Rubinov, Mikail and Jones, {P Simon} and V\'{e}rtes, Petra E and Ersche, Karen D and Suckling, John and Bullmore, Edward T},
journal = {NeuroImage},
pages = {287–304},
title = {{A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series}},
volume = {95},
year = {2014},
doi={http://dx.doi.org/10.1016/j.neuroimage.2014.03.012}
}


The code is available in 2 forms:

  • BrainWavelet Toolbox. This toolbox contains the Wavelet Despiking function described in the above paper, with a number of integrated options, and can be used as an add-on module to existing data pre-processing pipelines. This toolbox is available for both 4D brain imaging, NIfTI, data inputs (fMRI edition), and for 2D matrix input (Matrix edition). The Matrix edition was designed for despiking electrophysiological data. ** Available for download **
  •  fMRI Signal Processing Toolbox (SPT). This toolbox contains code for a full fMRI image processing pipeline. It takes as input, raw fMRI and anatomical datasets, and contains a variety of options to fully pre-process the data ready for subsequent analysis, such as seed-based connectivity or graph theoretic analysis. The Wavelet Despiking code (as in the BrainWavelet Toolbox) is included as an integrated option. This toolbox also contains parcellation templates and scripts to down-sample data (for graph theoretic analysis), movement diagnostic scripts to identify how badly subjects are affected by movement artefacts, and scripts for automatic detection of subjects for exclusion. ** Coming soon **

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