Papers › The Gaussian-Linear Hidden Markov model: a Python package
The Gaussian-Linear Hidden Markov model: a Python package
Diego Vidaurre, Laura Masaracchia, Nick Y. Larsen, Lenno R. P. T Ruijters, Sonsoles Alonso, Christine Ahrends, Mark W. Woolrich
We propose the Gaussian-Linear Hidden Markov model (GLHMM), a generalisation of different types of HMMs commonly used in neuroscience. In short, the GLHMM is a general framework where linear regression is used to flexibly parameterise the Gaussian state distribution, thereby accommodating a wide range of uses -- including unsupervised, encoding and decoding models. GLHMM is implemented as a Python toolbox with an emphasis on statistical testing and out-of-sample prediction -- i.e. aimed at finding and characterising brain-behaviour associations. The toolbox uses a stochastic variational inference approach, enabling it to handle large data sets at reasonable computational time. The approach can be applied to several data modalities, including animal recordings or non-brain data, and applied over a broad range of experimental paradigms. For demonstration, we show examples with fMRI, electrocorticography, magnetoencephalography and pupillometry.
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