osl_dynamics.analysis

Post-hoc analysis of inferred state/mode dynamics.

This subpackage provides tools for analysing the output of trained models (HMM states, DyNeMo modes, etc.) and for static (time-averaged) analysis.

Modules

• connectivity — Functional connectivity analysis (coherence, imaginary coherence, correlation).

• fisher_kernel — Fisher kernel for comparing HMM dynamics across sessions.

• post_hoc — Post-hoc spectral estimation of state/mode spectra using multitaper or regression methods.

• power — Power analysis (variance from spectra, band-limited power).

• prediction — Decoding/prediction using inferred dynamics.

• spectral — Spectral decomposition of state/mode covariances into power maps and coherence networks.

• static — Static (time-averaged) power and functional connectivity.

• statistics — Statistical testing (permutation tests, group comparisons).

Tutorials

• Static Spectral Analysis

• Static Power Analysis

• Static AEC Analysis

• Sliding Window Analysis

• HMM Multitaper Spectra

• HMM Summary Statistics

• HMM Plotting MEG Networks

• HMM fMRI Dual Estimation

• DyNeMo Regression Spectra

• DyNeMo Plotting Networks

• DyNeMo Mixing Coefficients

Python example scripts

• Spectra

• Static analysis

• Statistics

• MEG analysis

• fMRI analysis

• Decoding

• Plotting