Abstract
We present Visbrain, a Python open-source package that offers a comprehensive visualization suite for neuroimaging and electrophysiological brain data. Visbrain consists of two levels of abstraction: (1) objects which represent highly configurable neuro-oriented visual primitives (3D brain, sources connectivity, etc.) and (2) graphical user interfaces for higher level interactions. The object level offers flexible and modular tools to produce and automate the production of figures using an approach similar to that of Matplotlib with subplots. The second level visually connects these objects by controlling properties and interactions through graphical interfaces. The current release of Visbrain (version 0.4.2) contains 14 different objects and three responsive graphical user interfaces, built with PyQt: Signal, for the inspection of time-series and spectral properties, Brain for any type of visualization involving a 3D brain and Sleep for polysomnographic data visualization and sleep analysis. Each module has been developed in tight collaboration with end-users, i.e., primarily neuroscientists and domain experts, who bring their experience to make Visbrain as transparent as possible to the recording modalities (e.g., intracranial EEG, scalp-EEG, MEG, anatomical and functional MRI). Visbrain is developed on top of VisPy, a Python package providing high-performance 2D and 3D visualization by leveraging the computational power of the graphics card. Visbrain is available on Github and comes with a documentation, examples, and datasets (http://visbrain.org).
Highlights
The aim of scientific visualization is to graphically illustrate datasets—which are can be highly complex- in order to provide a better understanding and facilitate the interpretation of the data
Visbrain is in its early stages of development, but the present core should hopefully motivate users and programmers to contribute to the project and build a community-driven, powerful, sustainable, and full-featured open-source solution for brain data visualization
PR, AG, and KJ actively helped in the writing process
Summary
The aim of scientific visualization is to graphically illustrate datasets—which are can be highly complex- in order to provide a better understanding and facilitate the interpretation of the data. As scientific technologies continue to evolve, it becomes increasingly important to develop up-to-date and comprehensive visualization software capable of handling complex and large datasets. This is especially true in the field of neuroscience, which involves a myriad of neural recording types, and a wide and diverse range of possible data representations. Alternative visualization solutions that run on noncommercial open-source programming environments, such as Python, are rare These include high-quality packages such as MNE4 (Gramfort et al, 2013), PySurfer, Nilearn (Abraham et al, 2014) or 3d slicer (Fedorov et al, 2012). Some issues have been reported when installing Mayavi, (which uses VTK), which may affect its userfriendliness
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