EEG/MEG Source Connectivity Toolbox
SCoT is a Python package for EEG/MEG source connectivity estimation.
##### 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 is available online at http://scot-dev.github.io/scot-doc/index.html.
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/connectivity.py
PYTHONPATH=. python examples/misc/timefrequency.py
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.
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).