NeuroKit2: A Python Toolbox for Neurophysiological Signal Processing

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.