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Diego Vidaurre edited this page Nov 3, 2017 · 50 revisions

Introduction

HMM-MAR (Hidden Markov Model - Multivariate Autoregressive) is a toolbox to segment multivariate time series into states that are characterised by their unique quasi-stationary spectral properties. In the context of neuroscience applications, it can be used on both resting and task data, and on different data modalities (EEG, MEG, LFP, fMRI, etc). As a special case of the autoregressive model, a Gaussian distribution (using mean and/or covariance) is also implemented.

The toolbox comprises a number of additional features:

  • Estimation of the spectral properties for each state, using either a parametric (MAR) or a non-parametric approach (statewise multitaper).
  • Some preprocessing utilities, including correction for volume conduction.
  • Semi-supervised prediction of events.
  • Extension to the classical inference to work with very big data sets.
  • Routines for cross-validation and model selection.
  • Simulation of data from an HMM-MAR model.
  • Sign disambiguation for source reconstructed M/EEG data.

HMM-MAR is experimental software. The toolbox has no dependencies and only requires Matlab, unless:

  • The inputs are specified as SPM files, in which case it will require SPM.
  • Correction for volume conduction (aka signal leakage) is performed, in which case it requires the package MEG-ROI-nets.

Please cite the references below if this toolbox turns out to be useful. If there is something in this documentation that is not clear enough or does not make sense, please get in touch!

Documentation index

  1. Introduction
  2. Theory
  3. User Guide
  4. Examples

References

If this toolbox turns out to be useful, we'd grateful if you cite the main references for the HMM-MAR:

Diego Vidaurre, Andrew J. Quinn, Adam P. Baker, David Dupret, Alvaro Tejero-Cantero and Mark W. Woolrich (2016) Spectrally resolved fast transient brain states in electrophysiological data. NeuroImage. Volume 126, Pages 81–95.

and, describing an efficient inference (stochastic) method for big amounts of data,

Diego Vidaurre, R. Abeysuriya, R. Becker, Andrew J. Quinn, F. Alfaro-Almagro, S.M. Smith and Mark W. Woolrich (2017) Discovering dynamic brain networks from Big Data in rest and task. NeuroImage. In press

An example of application on fMRI is shown in

Diego Vidaurre, S.M. Smith and Mark W. Woolrich (2017). Brain network dynamics are hierarchically organized in time. Proceedings of the National Academy of Sciences of the USA. In press

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  1. Introduction
  2. Theory
  3. User Guide
  4. FAQ
  5. Examples
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