Please use this identifier to cite or link to this item: https://oxfordhealth-nhs.archive.knowledgearc.net/handle/123456789/1057
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dc.contributor.authorExternal author(s) only-
dc.date.accessioned2022-06-23T15:43:12Z-
dc.date.available2022-06-23T15:43:12Z-
dc.date.issued2022-02-
dc.identifier.citationCameron Higgins , Mats W.J. van Es, Andrew Quinn , Diego Vidaurre and Mark Woolrich. The relationship between frequency content and representational dynamics in the decoding of neurophysiological data. bioRxiv preprinten
dc.identifier.urihttps://oxfordhealth-nhs.archive.knowledgearc.net/handle/123456789/1057-
dc.description.abstractDecoding 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 35 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 40 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.en
dc.description.sponsorshipSupported by the NIHRen
dc.description.urihttps://doi.org/10.1101/2022.02.07.479399en
dc.language.isoenen
dc.subjectNeurophysiologyen
dc.subjectBrain Activityen
dc.titleThe relationship between frequency content and representational dynamics in the decoding of neurophysiological dataen
dc.typePreprinten
Appears in Collections:Neuroscience

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