Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/159711
Title: NeuroKit2: a Python toolbox for neurophysiological signal processing
Authors: Makowski, Dominique
Pham, Tam
Lau, Zen Juen
Brammer, Jan C.
Lespinasse, François
Pham, Hung
Schölzel, Christopher
Chen, Annabel Shen-Hsing
Keywords: Science::Medicine
Issue Date: 2021
Source: Makowski, D., Pham, T., Lau, Z. J., Brammer, J. C., Lespinasse, F., Pham, H., Schölzel, C. & Chen, A. S. (2021). NeuroKit2: a Python toolbox for neurophysiological signal processing. Behavior Research Methods, 53(4), 1689-1696. https://dx.doi.org/10.3758/s13428-020-01516-y
Journal: Behavior Research Methods
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.
URI: https://hdl.handle.net/10356/159711
ISSN: 1554-351X
DOI: 10.3758/s13428-020-01516-y
Rights: © 2021 The Psychonomic Society, Inc. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:LKCMedicine Journal Articles
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