NeuroKit2: a Python toolbox for neurophysiological signal processing

Dominique Makowski, Tam Pham, Zen J. Lau, Jan C. Brammer, Francois Lespinasse, Hung Pham, Christopher Scholzel, S. H. Annabel Chen

Research output: Contribution to journalArticlepeer-review

619 Citations (Scopus)

Abstract

NeuroKit2 is an open-source, community-driven, and user-centered Python package for neurophysiological signal processing. It provides a comprehensive suite of processing routines for a variety of bodily signals (e.g., ECG, PPG, EDA, EMG, RSP). These processing routines include high-level functions that enable data processing in a few lines of code using validated pipelines, which we illustrate in two examples covering the most typical scenarios, such as an event-related paradigm and an interval-related analysis. The package also includes tools for specific processing steps such as rate extraction and filtering methods, offering a trade-off between high-level convenience and fine-tuned control. Its goal is to improve transparency and reproducibility in neurophysiological research, as well as foster exploration and innovation. Its design philosophy is centred on user-experience and accessibility to both novice and advanced users.
Original languageEnglish
Pages (from-to)1689-1696
Number of pages8
JournalBehavior Research Methods
Volume53
Issue number4
DOIs
Publication statusPublished - Aug 2021
Externally publishedYes

Keywords

  • neurophysiology
  • biosignals
  • Python
  • ECG
  • EDA
  • EMG

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