The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
NeuroKit2: A Python toolbox for neurophysiological signal processing
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., et al. (2020) · Nature · doi:10.1038/s41586-020-2649-2 · Cited by 23,826
Estimating the reproducibility of psychological science
Open Science Collaboration (2015) · Science · doi:10.1126/science.aac4716 · Cited by 8,515
Kubios HRV–heart rate variability analysis software.
Tarvainen, M. P., Niskanen, J.-P., Lipponen, J. A., Ranta-aho, P. O., & Karjalainen, P. A. (2014) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2013.07.024 · Cited by 2,720
Deep learning-based electroencephalography analysis: a systematic review
Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T. H., & Faubert, J. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab260c · Cited by 1,532
HeartPy: A novel heart rate algorithm for the analysis of noisy signals
van Gent, P., Farah, H., van Nes, N., & van Arem, B. (2019) · Transportation Research Part F: Traffic Psychology and Behaviour · doi:10.1016/j.trf.2019.09.015 · Cited by 234
Research works printed but not linked: 21
- Bach, D. R. (2014). A head-to-head comparison of scralyze and ledalab, two model-based methods for skin conductance analysis. Biological Psychology, 103, 63–68. · DOI
- Bartels, R., & Pecanha, T. (2020). HRV: A pythonic package for heart rate variability analysis. Journal of Open Source Software, 5(51), 1867. https://doi.org/10.21105/joss.01867 · DOI
- Benedek, M., & Kaernbach, C. (2010). A continuous measure of phasic electrodermal activity. Journal of Neuroscience Methods, 190(1), 80–91. · DOI · Paper page
- Clifton, D. A., Gibbons, J., Davies, J., & Tarassenko, L. (2012). Machine learning and software engineering in health informatics. 2012 first international workshop on realizing ai synergies in software engineering (raise) (pp 37–41). IEEE. · DOI
- Gabrieli, G., Azhari, A., & Esposito, G. (2019). PySiology: A python package for physiological feature extraction. In Neural approaches to dynamics of signal exchanges (pp. 395–402). Springer Singapore. https://doi.org/10.1007/978-981-13-8950-4∖_35 · doi:10.1007/978-981-13-8950-4∖_35
- Gomes, P., Margaritoff, P., & Silva, H (2019). pyHRV: Development and evaluation of an open-source python toolbox for heart rate variability (hrv). Proc. Int’l conf On electrical, electronic and computing engineering (icetran), 822–828.
- Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Brodbeck, C., & et al. (2013). MEG and eeg data analysis with mne-python. Frontiers in Neuroscience, 7, 267. · DOI · Paper page
- Greco, A., Valenza, G., Lanata, A., Scilingo, E. P., & Citi, L. (2015). CvxEDA: A convex optimization approach to electrodermal activity processing. IEEE Transactions on Biomedical Engineering, 63(4), 797–804.
- Jupyter, B., Forde, F., Granger, H. W, Akici, F., Lippa, D., Niederhut, D., & Pacer, M. (2018). Binder 2.0 - Reproducible, interactive, sharable environments for science at scale. In Proceedings of the 17th Python in Science Conference. https://doi.org/%2010.25080/Majora-4af1f417-011%20 (pp. 113–120). · DOI · Paper page
- Khodadad, D., Nordebo, S., Mueller, B., Waldmann, A., Yerworth, R., Becher, T., & et al. (2018). Optimized breath detection algorithm in electrical impedance tomography. Physiological Measurement, 39(9), 094001. · doi:10.1088/1361-6579/aad7e6
- Kiverstein, J., & Miller, M. (2015). The embodied brain: Towards a radical embodied cognitive neuroscience. Frontiers in Human Neuroscience, 9, 237. · DOI
- Maizey, L., & Tzavella, L. (2019). Barriers and solutions for early career researchers in tackling the reproducibility crisis in cognitive neuroscience. Cortex, 113, 357–359. · DOI
- Makowski, D. (2020). Neurokit: A python toolbox for statistics and neurophysiological signal processing (eeg, eda, ecg, emg...). Retrieved from https://github.com/neuropsychology/NeuroKit.py · DOI
- Marchewka, A., Żurawski, J.K., & Grabowska, A. (2014). The nencki affective picture system (naps): Introduction to a novel, standardized, wide-range, high-quality, realistic picture database. Behavior Research Methods, 46(2), 596–610. · DOI · Paper page
- McKinney, W., et al. (2010). Data structures for statistical computing in python. Proceedings of the 9th python in science conference (vol. 445, pp. 51–56). Austin. · DOI · Paper page
- Miłkowski, M., Hensel, W. M., & Hohol, M. (2018). Replicability or reproducibility? on the replication crisis in computational neuroscience and sharing only relevant detail. Journal of Computational Neuroscience, 45(3), 163–172. · DOI
- Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
- Quintana, D., Alvares, G. A., & Heathers, J. (2016). Guidelines for reporting articles on psychiatry and heart rate variability (graph): Recommendations to advance research communication. Translational Psychiatry, 6(5), e803–e803. · DOI · Paper page
- Topalidou, M., Leblois, A., Boraud, T., & Rougier, N. (2015). P A long journey into reproducible computational neuroscience. Frontiers in Computational Neuroscience, 9, 30. · DOI
- Van Rossum, G., & Drake, F. L. (2009) Python 3 reference manual. CreateSpace: Scotts Valley.
- Yuehong, Y., Zeng, Y., Chen, X., & Fan, Y. (2016). The internet of things in healthcare: An overview. Journal of Industrial Information Integration, 1, 3–13. · DOI · Paper page
Cited by
Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling
Mayourian, J., La Cava, W. G., Vaid, A., Nadkarni, G. N., Ghelani, S. J., Mannix, R., Geva, T., Dionne, A., Alexander, M. E., Duong, S. Q., et al. (2024) · Circulation · doi:10.1161/circulationaha.123.067750 · Cited by 59
(2025) · doi:10.1109/jbhi.2023.3346205 · Cited by 47
Marshall, A. C., Gentsch-Ebrahimzadeh, A., & Schütz-Bosbach, S. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119011 · Cited by 36
Dynamic alignment and fusion of multimodal physiological patterns for stress recognition
Zhang, X., Wei, X., Zhou, Z., Zhao, Q., Zhang, S., Yang, Y., Li, R., & Hu, B. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2023.3290177 · Cited by 38
A multimodal driver monitoring benchmark dataset for driver modeling in assisted driving automation
Dargahi Nobari, K., & Bertram, T. (2024) · Scientific Data · doi:10.1038/s41597-024-03137-y · Cited by 29
Metadata
- Authors
- Makowski, D., Pham, T., Lau, Z. J., Brammer, J. C., Lespinasse, F., Pham, H., Schölzel, C., & Chen, S. H. A.
- Venue
- Behavior Research Methods
- Published
- 2021-02-02
- Volume
- 53
- Issue
- 4
- Pages
- 1689-1696
- ISSN
- 1554-351X (ISSN-L)
- 1554-3528
- OpenAlex
- W3127637041
- Cited by
- 1,388
- References
- 7
- FWCI
- 159.28
- Type
- journal article
- Language
- en
- Open access
- bronze
- Licence
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: NeuroKit2: A Python toolbox for neurophysiological signal processing. https://app.cochat.ai/papers/10.3758/s13428-020-01516-y
- The paper
- Makowski, D., Pham, T., Lau, Z. J., Brammer, J. C., Lespinasse, F., Pham, H., Schölzel, C., & Chen, S. H. A. (2021). NeuroKit2: A Python toolbox for neurophysiological signal processing. Behavior Research Methods. https://doi.org/10.3758/s13428-020-01516-y
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
