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:
Processing M/EEG data: preprocessing, source reconstruction and parcellation.
Inferring and visualising dynamic (and static) functional networks.
Spectral estimation: including multitaper spectra and wavelet transforms.
Burst detection.
Statistical significance testing (using GLM permutations).
Simulating time series data (e.g. HMMs, autoregressive models, etc).
And much more!
For more information on how to use osl-dynamics see the documentation.
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.
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 this paper:
Gohil, C., Huang, R., Roberts, E., van Es, M. W., Quinn, A. J., Vidaurre, D., & Woolrich, M. W. (2024). osl-dynamics, a toolbox for modeling fast dynamic brain activity. Elife, 12, RP91949.