Key result
NeuroKit2 provides open-source Python functions for processing neurophysiological signals like ECG and PPG.
Why the study?
NeuroKit2 was developed to provide a comprehensive tool for simultaneous processing of diverse neurophysiological signals, aiming to improve transparency and reproducibility in research.
NeuroKit2 is an open-source Python toolbox that facilitates accessible and reproducible processing of neurophysiological signals such as ECG.
May facilitate reproducible ECG and related signal analysis in research; leaves open clinical validation and workflow integration.
NeuroKit2 is an open-source, community-driven, and user-friendly Python package dedicated to neurophysiological signal processing with an initial focus on bodily signals (e.g., ECG, PPG, EDA, EMG, RSP). Its design philosophy is centred on user-experience and accessibility to both novice and advanced users. The package provides a consistent set of 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 dedicated to specific processing steps such as rate extraction and filtering methods, offering a trade-off between efficiency and fine-tuned control to the user. Rather than focusing on specific signals, NeuroKit2 was developed to provide a comprehensive means for a simultaneous processing of a wide range of signals. Its goal is to improve transparency and reproducibility in neurophysiological research, as well as foster exploration and innovation.
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Makowski et al. (2020) studied Neurophysiological signal processing. NeuroKit2 Python package was evaluated. NeuroKit2 is an open-source Python package that provides a consistent set of high-level functions for processing neurophysiological signals like ECG, PPG, EDA, EMG, and RSP.
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