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MNE-Connectivity 0.8.0.dev71+gf9c4df1d documentation

  • What’s new?
  • Installation
  • API
  • Examples
  • GitHub
  • What’s new?
  • Installation
  • API
  • Examples
  • GitHub

Section Navigation

  • Comparing PLI, wPLI, and dPLI
  • Comparing spectral connectivity computed over time or over trials
  • Comparison of coherency-based methods
  • Compute Phase Slope Index (PSI) in source space for a visual stimulus
  • Compute all-to-all connectivity in sensor space
  • Compute coherence in source space using a MNE inverse solution
  • Compute directionality of connectivity with multivariate Granger causality
  • Compute envelope correlations in source space
  • Compute envelope correlations in volume source space
  • Compute full spectrum source space connectivity between labels
  • Compute mixed source space connectivity and visualize it using a circular graph
  • Compute multivariate coherency/coherence
  • Compute multivariate measures of the imaginary part of coherency
  • Compute seed-based time-frequency connectivity in sensor space
  • Compute source space connectivity and visualize it using a circular graph
  • Determine the significance of connectivity estimates against baseline connectivity
  • Using the connectivity classes
  • Working with ragged indices for multivariate connectivity
  • Decoding & Decomposition Examples
    • Multivariate decomposition for efficient connectivity analysis
    • Visualising spatial contributions to multivariate connectivity
  • Dynamic Connectivity Examples
    • Compute vector autoregressive model (linear system)
  • Examples
  • Decoding & Decomposition Examples

Decoding & Decomposition Examples#

Examples demonstrating multivariate connectivity analysis using the decomposition tools of the decoding module.

Multivariate decomposition for efficient connectivity analysis

Multivariate decomposition for efficient connectivity analysis

Visualising spatial contributions to multivariate connectivity

Visualising spatial contributions to multivariate connectivity

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Working with ragged indices for multivariate connectivity

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Multivariate decomposition for efficient connectivity analysis

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