Authors

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Core team

Contributors

Thanks also to Gansheng Tan, Chuan-Peng Hu, @ucohen, Anthony Gatti, Julien Lamour, @renatosc, Nicolas Beaudoin-Gagnon and @rubinovitz for their contribution in NeuroKit 1.

How to Cite

Makowski, D., Pham, T., Lau, Z. J., Brammer, J. C., Lespinasse, F., Pham, H., Schölzel, C., & Chen, S. A. (2021). NeuroKit2: A Python toolbox for neurophysiological signal processing. Behavior Research Methods. https://doi.org/10.3758/s13428-020-01516-y

Full bibtex reference:

@article{Makowski2021neurokit,
  author = {Makowski, Dominique and Pham, Tam and Lau, Zen J. and Brammer, Jan C. and Lespinasse, Fran{\c{c}}ois and Pham, Hung and Sch{\"o}lzel, Christopher and Chen, S. H. Annabel},
  title={NeuroKit2: A Python toolbox for neurophysiological signal processing},
  journal={Behavior Research Methods},
  year={2021},
  month={Feb},
  day={02},
  abstract={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.},
  issn={1554-3528},
  doi={10.3758/s13428-020-01516-y},
  url={https://doi.org/10.3758/s13428-020-01516-y}
}