NeuroKit2: A Python toolbox for neurophysiological signal processing

By Dominique Makowski, Tam Pham, Z. J. Lau, Jan C. Brammer, François Lespinasse, Pham Tien Hung, Christopher Schölzel, Shen-Hsing Annabel Chen. Behavior Research Methods, 2020

1,357 citations recorded by the source corpus.

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.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/a6a73a550cd1cd5de3e6f7c53a00cf46193cf868

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. Changkun Ou
2. Sven Mayer
3. Francesco Chiossi

In Cognitive Neuroscience

1. Changkun Ou
2. Sven Mayer
3. Francesco Chiossi

Citations in the corpus

• Exploring Physiological Correlates of Visual Complexity Adaptation: Insights from EDA, ECG, and EEG Data for Adaptation Evaluation in VR Adaptive Systems (2023, methodology): “We processed EDA data via the Neurokit toolbox [41].”