Overview

This is the documentation of SCoT, the EEG source connectivity toolbox in Python. SCoT provides functionality for blind source decomposition and connectivity estimation. Connectivity is estimated from spectral measures (such as COH(), PDC(), or DTF()) using vector autoregressive (VAR) models.

Note that the documentation is work-in-progress. Most sections are still missing, but we will add them in the near future. However, the API Reference is in a usable state.

Note

As of version 0.2, the data format in all SCoT routines has changed. It is now consistent with Scipy and MNE-Python. Specifically, epoched input data is now arranged in three-dimensional arrays of shape (epochs, channels, samples). In addition, continuous data is now arranged in two-dimensional arrays of shape (channels, samples).

Contents

• About SCoT
  • Introduction
  • Acknowledgments
  • SCoT Examples
  • Gallery
• Manual
  • Getting started
• API Reference
  • scot
• Miscellaneous Topics
  • Is VAR model fitting invariant to PCA transformations?
  • Testing SPHINX Features