Result: NeuroKit2: A Python toolbox for neurophysiological signal processing

Title:
NeuroKit2: A Python toolbox for neurophysiological signal processing
Authors:
Makowski, D., Pham, T., Lau, Z.J., Brammer, J.C., Lespinasse, F., Pham, H., Schölzel, C., Chen, S.H.A.
Source:
Behavior Research Methods; 1689; 1696; 1554-351X; 4; 53; ~Behavior Research Methods~1689~1696~~~1554-351X~4~53~~
Publisher Information:
2021
Document Type:
Electronic Resource Electronic Resource
Availability:
Open access content. Open access content
Other Numbers:
NLQGE oai:repository.ubn.ru.nl:2066/230586
https://hdl.handle.net/2066/230586
https://doi.org/10.3758/s13428-020-01516-y
1247243209
Contributing Source:
RADBOUD UNIVERSITEIT NAJMEGEN
From OAIster®, provided by the OCLC Cooperative.
Accession Number:
edsoai.on1247243209
Database:
OAIster

Further Information

Contains fulltext : 230586.pdf (Publisher’s version ) (Closed access)
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.