SCoT: a Python toolbox for EEG source connectivity

Martin Billinger, Clemens Brunner*, Gernot Müller-Putz

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Analysis of brain connectivity has become an important research tool in neuroscience. Connectivity can be estimated between cortical sources reconstructed from the electroencephalogram (EEG). Such analysis often relies on trial averaging to obtain reliable results. However, some applications such as brain-computer interfaces (BCIs) require single-trial estimation methods. In this paper, we present SCoT—a source connectivity toolbox for Python. This 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. We demonstrate basic usage of SCoT on motor imagery (MI) data. Furthermore, we show simulation results of utilizing SCoT for feature extraction in a BCI application. These results indicate that CSPVARICA and correct regularization can significantly improve MI classification. While SCoT was mainly designed for application in BCIs, it contains useful tools for other areas of neuroscience. SCoT is a software package that (1) brings combined source decomposition and connectivtiy estimation to the open Python platform, and (2) offers tools for single-trial connectivity estimation. The source code is released under the MIT license and is available online at github.com/SCoT-dev/SCoT.
Original languageEnglish
Article number22
Number of pages11
JournalFrontiers in Neuroinformatics
Volume8
DOIs
Publication statusPublished - 2014

Fields of Expertise

  • Human- & Biotechnology

Treatment code (Nähere Zuordnung)

  • Application

Fingerprint

Dive into the research topics of 'SCoT: a Python toolbox for EEG source connectivity'. Together they form a unique fingerprint.

Cite this