Recommendations and publication guidelines for studies using frequency domain and time-frequency domain analyses of neural time series

Andreas Keil, Edward M. Bernat, Michael X. Cohen, Mingzhou Ding, Monica Fabiani, Gabriele Gratton, Emily S. Kappenman, Eric Maris, Kyle E. Mathewson, Richard T. Ward, Nathan Weisz

Research output: Contribution to journalArticlepeer-review


Since its beginnings in the early 20th century, the psychophysiological study of human brain function has included research into the spectral properties of electrical and magnetic brain signals. Now, dramatic advances in digital signal processing, biophysics, and computer science have enabled increasingly sophisticated methodology for neural time series analysis. Innovations in hardware and recording techniques have further expanded the range of tools available to researchers interested in measuring, quantifying, modeling, and altering the spectral properties of neural time series. These tools are increasingly used in the field, by a growing number of researchers who vary in their training, background, and research interests. Implementation and reporting standards also vary greatly in the published literature, causing challenges for authors, readers, reviewers, and editors alike. The present report addresses this issue by providing recommendations for the use of these methods, with a focus on foundational aspects of frequency domain and time-frequency analyses. It also provides publication guidelines, which aim to (1) foster replication and scientific rigor, (2) assist new researchers who wish to enter the field of brain oscillations, and (3) facilitate communication among authors, reviewers, and editors.

Original languageEnglish (US)
Article numbere14052
Issue number5
StatePublished - May 2022


  • EEG
  • electrophysiology
  • frequency domain analysis
  • MEG
  • time-frequency analysis

ASJC Scopus subject areas

  • General Neuroscience
  • Neuropsychology and Physiological Psychology
  • Experimental and Cognitive Psychology
  • Neurology
  • Endocrine and Autonomic Systems
  • Developmental Neuroscience
  • Cognitive Neuroscience
  • Biological Psychiatry


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