Connectionists: EEGraph (Python neuroscience package) Update

[Alberto Nogales]

Dear Connectionists Community,

I hope this message finds you well.

I would like to share a new version of *EEGraph*, an open-source Python
library designed to facilitate the construction and analysis of functional
brain connectivity networks from EEG recordings.

EEGraph was originally developed to provide researchers with a simple,
transparent, and reproducible framework for transforming EEG signals into
graph representations that can be readily integrated into network
neuroscience and machine learning workflows. The library supports multiple
connectivity metrics and graph-generation pipelines while aiming to reduce
the technical barriers associated with EEG network analysis.

In this latest release, EEGraph has been significantly extended beyond its
original scope. New features include additional connectivity estimation
methods, improved preprocessing and analysis capabilities, enhanced
flexibility for different experimental settings, better documentation, and
several performance and usability improvements. These developments aim to
make EEGraph a more comprehensive tool for researchers working in
computational neuroscience, brain network analysis, neuroengineering, and
related fields.

As with any open-source scientific software, community feedback is
invaluable. We encourage researchers, students, and developers to try the
new version in their own projects, report any issues they encounter,
suggest improvements, and contribute to future development. Identifying
bugs, edge cases, or unexpected behaviours is particularly helpful for
improving the reliability and robustness of the package.

The project is available here: https://github.com/ufvceiec/EEGRAPH

Documentation, examples, and installation instructions can be found in the
repository.

PS: We are also pleased to mention that work is currently underway to
extend EEGraph beyond EEG data. A forthcoming release will incorporate
support for fMRI-based functional connectivity analysis, enabling
researchers to construct and analyse brain networks from both
electrophysiological and neuroimaging modalities within a unified
framework. We hope this expansion will further broaden the applicability of
EEGraph and foster multimodal neuroscience research.

Thank you for your time and interest.

Best regards, Alberto Nogales
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/connectionists/attachments/20260624/ca05912f/attachment.html>