Toolbox HMM-MAR

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. 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).
• 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 only requires Matlab and is experimental software.

Please refer to the HMM-MAR wiki for more information and to obtain the code.