The GLHMM toolbox provides facilities to fit a variety of Hidden Markov models (HMM) based on the Gaussian distribution, which we generalise as the Gaussian-Linear HMM. The toolbox has a focus on finding associations at various levels between brain data (EEG, MEG, fMRI, ECoG, etc) and non-brain data, such as behavioural or physiological variables. A good starting point to decide what is the best way to set it up is https://github.com/vidaurre/glhmm/blob/main/docs/notebooks/tutorial.ipynb
Important links¶
Official source code repo: https://github.com/vidaurre/glhmm
Jupyter notebooks with examples: https://github.com/vidaurre/glhmm/tree/main/docs/notebooks
GLHMM documentation: https://glhmm.readthedocs.io/en/latest/index.html
Paper: https://direct.mit.edu/imag/article/doi/10.1162/imag_a_00460/127499
In addition to using the GLHMM toolbox as a Python package, a graphical user interface (GUI) is now available. The GUI offers an intuitive, code-free way to load data, train models, run statistical tests, and visualise results. To access the GUI, visit the companion repository: https://github.com/Nick7900/glhmm_protocols
The GUI is built using Streamlit and can be launched locally. Instructions for setup and use are provided in that repository. An introductory walkthrough of the GUI is available here:
GLHMM GUI Tutorial – YouTube
Dependencies¶
The required dependencies to use glhmm are:
Python >= 3.10
NumPy
numba
scikit-learn
scipy
matplotlib
seaborn
pickle
scikit-learn
cupy (only when using GPU acceleration; requires manual install)
h5py
Installation¶
To install the latest development version from the repository, use the following command:
pip install git+https://github.com/vidaurre/glhmm
Alternatively, to install the latest stable release from PyPI, use the command:
pip install glhmm
Documentation¶
Warning The documentation of this library is under development
Contents: