NeuroKit2: a Python toolbox for neurophysiological signal processing

• Main Authors: Makowski, Dominique, Pham, Tam, Lau, Zen Juen, Brammer, Jan C., Lespinasse, François, Pham, Hung, Schölzel, Christopher, Chen, Annabel Shen-Hsing
• Other Authors: School of Social Sciences
• Format: Article
• Language: English
• Published: 2022
• Subjects: Science::Medicine; Neurophysiology; Biosignals
• Online Access: https://hdl.handle.net/10356/159711
• Institution: Nanyang Technological University

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.