4.7 Article

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

期刊

NEUROIMAGE
卷 260, 期 -, 页码 -

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2022.119462

关键词

Representational dynamics; Decoding; Aliasing; Complex spectrum decoding

资金

  1. Wellcome Trust [203139/Z/16/Z, 106183/Z/14/Z, 215573/Z/19/Z]
  2. UK MRC
  3. Dementia Platform UK [RG94383/RG89702]
  4. NIHR Oxford Health Biomedical Research Centre
  5. Novo Nordisk Emerging Investigator Award [NNF19OC-0054895]
  6. European Research Council [ERC-StG2019-850404]
  7. EU-project euSNN [MSCA-ITN H2020860563]

向作者/读者索取更多资源

This study investigates the relationship between high temporal resolution, stimulus-evoked neurophysiological data decoding and frequency spectra. The authors find that in instantaneous signal decoding paradigms, each sinusoidal component of the evoked response is translated to double its original frequency in the subsequent decoding accuracy timecourse. They recommend applying more aggressive low pass filtering in these paradigms to eliminate representational alias artefacts. Additionally, the authors propose using both the instantaneous magnitude and local gradient information of the signal as features for decoding to overcome interpretational challenges.
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.

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