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Porthole and Stormcloud: Tools for visualisation of spatiotemporal M/EEG statistics

View ORCID ProfileJeremy A Taylor, Marta I Garrido
doi: https://doi.org/10.1101/534784
Jeremy A Taylor
1Queensland Brain Institute, The University of Queensland, Australia
2Centre for Advanced Imaging, The University of Queensland, Australia
3School of Information Technology and Electrical Engineering, The University of Queensland, Australia
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  • ORCID record for Jeremy A Taylor
Marta I Garrido
1Queensland Brain Institute, The University of Queensland, Australia
2Centre for Advanced Imaging, The University of Queensland, Australia
4School of Mathematics and Physics, The University of Queensland, Australia
5Australian Research Council Centre of Excellence for Integrative Brain Function, Australia
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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 currently 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:

  1. Animated two-dimensional scalp maps with dynamic timeline annotation and optional user interaction;

  2. 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 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.

Footnotes

  • Conflict of Interest: The authors declare no competing financial interests.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted January 30, 2019.
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Porthole and Stormcloud: Tools for visualisation of spatiotemporal M/EEG statistics
Jeremy A Taylor, Marta I Garrido
bioRxiv 534784; doi: https://doi.org/10.1101/534784
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Porthole and Stormcloud: Tools for visualisation of spatiotemporal M/EEG statistics
Jeremy A Taylor, Marta I Garrido
bioRxiv 534784; doi: https://doi.org/10.1101/534784

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