NeuroKit2: A Python toolbox for neurophysiological signal processing

• Published in: Behavior research methods Vol. 53; no. 4; pp. 1689 - 1696
• Main Authors: Makowski, Dominique, Pham, Tam, Lau, Zen J., Brammer, Jan C., Lespinasse, François, Pham, Hung, Schölzel, Christopher, Chen, S. H. Annabel
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2021; Springer Nature B.V
• Subjects: Behavioral Science and Psychology; Cognitive Psychology; Data processing; EKG; Psychology; Signal processing; Biosignals; ECG; EDA; EMG; Neurophysiology; Python
• ISSN: 1554-3528; 1554-3528
• DOI: 10.3758/s13428-020-01516-y

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.