SCoT is a Python package for EEG/MEG source connectivity estimation.
Use the following command to install SCoT from PyPi:
pip install scot
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 >= 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.
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.
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).