The Gaussian-Linear Hidden Markov model: a Python package
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arXiv
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| Format: | Preprint |
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2023
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| author | Vidaurre, Diego Masaracchia, Laura Larsen, Nick Y. Ruijters, Lenno R. P. T Alonso, Sonsoles Ahrends, Christine Woolrich, Mark W. |
| author_facet | Vidaurre, Diego Masaracchia, Laura Larsen, Nick Y. Ruijters, Lenno R. P. T Alonso, Sonsoles Ahrends, Christine Woolrich, Mark W. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_07151 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The Gaussian-Linear Hidden Markov model: a Python package Vidaurre, Diego Masaracchia, Laura Larsen, Nick Y. Ruijters, Lenno R. P. T Alonso, Sonsoles Ahrends, Christine Woolrich, Mark W. Neurons and Cognition Machine Learning 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. |
| title | The Gaussian-Linear Hidden Markov model: a Python package |
| topic | Neurons and Cognition Machine Learning |
| url | https://arxiv.org/abs/2312.07151 |