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Porthole and Stormcloud: Tools for Visualisation of Spatiotemporal M/EEG Statistics

  • Jeremy A TaylorEmail author
  • Marta I Garrido
Software Original Article

Abstract

Electro- and magneto-encephalography are functional neuroimaging modalities characterised by their ability to quantify dynamic spatiotemporal activity within the brain. However, the visualisation techniques used to illustrate these effects are currently limited to single- or multi-channel time series plots, topographic scalp maps and orthographic cross-sections of the spatiotemporal data structure. Whilst these methods each have their own strength and weaknesses, they are only able to show a subset of the data and are suboptimal at articulating one or both of the space-time components.

Here, we propose Porthole and Stormcloud, a set of data visualisation tools which can automatically generate context appropriate graphics for both print and screen with the following graphical capabilities:
  • Animated two-dimensional scalp maps with dynamic timeline annotation and optional user interaction;

  • Three-dimensional construction of discrete clusters within sparse spatiotemporal volumes, rendered with ‘cloud-like’ appearance and augmented by cross-sectional scalp maps indicating local maxima.

These publicly available tools were designed specifically for visualisation of M/EEG spatiotemporal statistical parametric maps, however, we also demonstrate alternate use cases of posterior probability maps and weight maps produced by machine learning classifiers. In principle, the methods employed here are transferrable to visualisation of any spatiotemporal image.

Keywords

EEG MEG Space-time cube Visualisation Statistical parametric mapping Machine learning 

Notes

Acknowledgements

This work was supported by the Australian Research Council Centre of Excellence for Integrative Brain Function (ARC Centre Grant CE140100007), a University of Queensland Fellowship (2016000071) and a Foundation Research Excellence Award (2016001844) to MIG. We would like to thank Tyler Hobson for discussions on computer graphics methods, Clare Harris for providing data, as well as Veronika Halász, Kit Melissa Larsen, Ilvana Dzafic, Jessica McFadyen and Chase Sherwell for providing feedback on the functionality of earlier versions of the toolbox.

Compliance with Ethical Standards

Conflict of Interest

The authors declare no competing financial interests.

Supplementary material

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Melbourne School of Psychological SciencesUniversity of MelbourneMelbourneAustralia
  2. 2.Queensland Brain InstituteUniversity of QueenslandBrisbaneAustralia
  3. 3.Centre for Advanced ImagingUniversity of QueenslandBrisbaneAustralia
  4. 4.School of Information Technology and Electrical EngineeringUniversity of QueenslandBrisbaneAustralia
  5. 5.School of Mathematics and PhysicsUniversity of QueenslandBrisbaneAustralia
  6. 6.Australian Research Council Centre of Excellence for Integrative Brain FunctionMelbourneAustralia

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