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

Abstract

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 signals 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. HighlightsO_LIWe investigate different decoding paradigms applied to epoched data and characterise the information content available to each over time. C_LIO_LIUnder commonly used instantaneous signal decoding paradigms, sinusoidal components of the evoked response are translated to double their original frequency in decoding accuracy metrics, presenting technical and interpretational challenges. C_LIO_LIWhen instantaneous signal decoding is used, we recommend using low pass filters with a cut-off less than one quarter of the sampling rate to eliminate spurious representational alias artefacts. C_LIO_LIThe interpretational issues associated with instantaneous signal decoding can be resolved with alternative paradigms such as complex spectrum decoding. C_LIO_LIWe show that complex spectrum decoding results in decoding accuracy metrics that are higher, more stable over time, and free of representational aliasing. C_LI