SCoT is a python toolbox for EEG analysis which allows estimating connectivity between cortical sources that are reconstructed from EEG. The toolbox implements routines for blind source decomposition and connectivity estimation with the MVARICA approach. Additionally, a novel extension called CSPVARICA is available for labeled data. SCoT estimates connectivity from various spectral measures relying on vector autoregressive (VAR) models. Optionally, these VAR models can be regularized to facilitate ill posed applications such as single-trial fitting. While SCoT was mainly designed for application in BCIs, it contains useful tools for other areas of neuroscience. SCoT is a software package that (i) brings combined source decomposition and connectivity estimation to the open Python platform, and (ii) offers tools for single-trial connectivity estimation.

Download the publication.

Download the software.