The relationship between frequency content and representational dynamics in the decoding of neurophysiological data

Cameron Higgins, Mats W.J. van Es*, Andrew J. Quinn, Diego Vidaurre, Mark W. Woolrich

*Corresponding author for this work

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Decoding of high temporal resolution, stimulus-evoked neurophysiological data is increasingly used to test theories about how the brain processes information. However, a fundamental relationship between the frequency spectra of the neural signal and the subsequent decoding accuracy timecourse is not widely recognised. We show that, in commonly used instantaneous signal decoding paradigms, each sinusoidal component of the evoked response is translated to double its original frequency in the subsequent decoding accuracy timecourses. We therefore recommend, where researchers use instantaneous signal decoding paradigms, that more aggressive low pass filtering is applied with a cut-off at one quarter of the sampling rate, to eliminate representational alias artefacts. However, this does not negate the accompanying interpretational challenges. We show that these can be resolved by decoding paradigms that utilise both a signal's instantaneous magnitude and its local gradient information as features for decoding. On a publicly available MEG dataset, this results in decoding accuracy metrics that are higher, more stable over time, and free of the technical and interpretational challenges previously characterised. We anticipate that a broader awareness of these fundamental relationships will enable stronger interpretations of decoding results by linking them more clearly to the underlying signal characteristics that drive them.

Original languageEnglish
Article number119462
Publication statusPublished - 28 Jul 2022

Bibliographical note

Funding Information:
This research was funded by the Wellcome Trust ( 106183/Z/14/Z , 215573/Z/19/Z ), the New Therapeutics in Alzheimer's Diseases ( NTAD ) study supported by UK MRC and the Dementia Platform UK ( RG94383/RG89702 ) and the EU-project euSNN ( MSCA-ITN H2020-860563 ), and supported by the NIHR Oxford Health Biomedical Research Centre . The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust ( 203139/Z/16/Z ). DV is supported by a Novo Nordisk Emerging Investigator Award ( NNF19OC-0054895 ) and by the European Research Council ( ERC-StG-2019-850404 ). For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.

Publisher Copyright:
© 2022


  • Aliasing
  • Complex spectrum decoding
  • Decoding
  • Representational dynamics
  • Brain Mapping/methods
  • Humans
  • Brain/physiology
  • Neurophysiology

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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