EEG/MEG Source Connectivity Toolbox in Python
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README.md

Python 2.7 Python 3.5 License Build Status Coverage Status

SCoT

SCoT is a Python package for EEG/MEG source connectivity estimation.

Obtaining SCoT

From PyPi

Use the following command to install SCoT from PyPi:

pip install scot
From Source

Use the following command to fetch the sources:

git clone --recursive https://github.com/scot-dev/scot.git scot

The flag --recursive tells git to check out the numpydoc submodule, which is required for building the documentation.

Documentation

Documentation is available online at http://scot-dev.github.io/scot-doc/index.html.

Dependencies

Required: numpy >= 1.8.2, scipy >= 0.13.3

Optional: matplotlib >= 1.4.0, scikit-learn >= 0.15.0, mne >= 0.11.0

Lower versions of these packages may work but are not tested.

Examples

To run the examples on Linux, invoke the following commands inside the SCoT main directory:

PYTHONPATH=. python examples/misc/connectivity.py

PYTHONPATH=. python examples/misc/timefrequency.py

etc.

Note that you need to obtain the example data from https://github.com/SCoT-dev/scot-data. The scot-data package must be on Python's search path.

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).