Skip to main content

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).

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

scot-0.2.1-py2.py3-none-any.whl (57.7 kB view hashes)

Uploaded Python 2 Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page