OSL Dynamics Toolbox

This package contains models for analysing neuroimaging data. In particular, this package contains methods (based on generative models) for studying dynamics in time series data. This package can be used for:

• Inferring and visualising dynamic (and static) functional networks.

• Spectral estimation: including multitaper spectra and wavelet transforms.

• Burst detection.

• Statistical significant testing (using GLM permutation testing).

• Simulating time series data (e.g. HMMs, autoregressive models, etc).

• And much more!

Models included:

• Hidden Markov Model (HMM).

• Dynamic Network Modes (DyNeMo).

For more information on how to use osl-dynamics see the documentation.

This package was developed by the Oxford Centre for Human Brain Activity (OHBA) Methods Group at the University of Oxford. Our group website is here.

If you find this toolbox useful, please cite:

Gohil C., Huang R., Roberts E., van Es M.W.J., Quinn A.J., Vidaurre D., Woolrich M.W. (2023) osl-dynamics: A toolbox for modelling fast dynamic brain activity. eLife 12:RP91949 https://doi.org/10.7554/eLife.91949.2

If you would like to request new features or if you’re confident that you have found a bug, please create a new issue on the GitHub issues page.

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Contents

• Install
  • Linux Instructions
  • Windows Instructions
  • Training Speed
• Documentation
  • Models
  • Tutorials
  • Workshops
• FAQ
  • Installation
  • Data
  • Modelling
  • Post-Hoc Analysis
  • Other
• API Reference
  • osl_dynamics

Indices and tables

• Index

• Module Index

• Search Page