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Spatiotemporally resolved multivariate pattern analysis for M/EEG

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  • Cameron Higgins, University of Oxford
  • ,
  • Diego Vidaurre
  • Nils Kolling, University of Oxford
  • ,
  • Yunzhe Liu, Beijing Normal University, Chinese Institute for Brain Research, Max Planck University College London Centre for Computational Psychiatry and Ageing Research
  • ,
  • Tim Behrens, University of Oxford, University College London
  • ,
  • Mark Woolrich, University of Oxford

An emerging goal in neuroscience is tracking what information is represented in brain activity over time as a participant completes some task. While electroencephalography (EEG) and magnetoencephalography (MEG) offer millisecond temporal resolution of how activity patterns emerge and evolve, standard decoding methods present significant barriers to interpretability as they obscure the underlying spatial and temporal activity patterns. We instead propose the use of a generative encoding model framework that simultaneously infers the multivariate spatial patterns of activity and the variable timing at which these patterns emerge on individual trials. An encoding model inversion maps from these parameters to the equivalent decoding model, allowing predictions to be made about unseen test data in the same way as in standard decoding methodology. These SpatioTemporally Resolved MVPA (STRM) models can be flexibly applied to a wide variety of experimental paradigms, including classification and regression tasks. We show that these models provide insightful maps of the activity driving predictive accuracy metrics; demonstrate behaviourally meaningful variation in the timing of pattern emergence on individual trials; and achieve predictive accuracies that are either equivalent or surpass those achieved by more widely used methods. This provides a new avenue for investigating the brain's representational dynamics and could ultimately support more flexible experimental designs in the future.

Original languageEnglish
JournalHuman Brain Mapping
Pages (from-to)3062-3085
Number of pages24
Publication statusPublished - Jul 2022

Bibliographical note

Publisher Copyright:
© 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

    Research areas

  • decoding, EEG, encoding, MEG, single trial task dynamics

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