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EEG/MEG Source Connectivity Toolbox

Project Description

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 scot

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


Documentation is available online at


Required: numpy, scipy

Optional: matplotlib, scikit-learn

The lowest supported versions of these libraries are numpy 1.8.0, scipy 0.13.3, scikit-learn 0.15.0, and matplotlib 1.4.0. Lower versions may work but are not tested.


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

PYTHONPATH=. python examples/misc/

PYTHONPATH=. python examples/misc/


Note that you need to obtain the example data from The scot-data package must be on Python’s search path.


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).

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Filename, Size & Hash SHA256 Hash Help File Type Python Version Upload Date
(57.7 kB) Copy SHA256 Hash SHA256
Wheel 3.4 Feb 9, 2016

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