Software:NeuroKit

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NeuroKit
NeuroKit2 logo.png
Written inPython
Operating systemAll OS supported by Python
Available inEnglish
TypeStatistical software
LicenseMIT License
Websitegithub.com/neuropsychology/NeuroKit

NeuroKit ("nk") is an open source toolbox for physiological signal processing.[1] The most recent version, NeuroKit2, is written in Python and is available from the PyPI package repository.[2] As of June 2022, the software was used in 94 scientific publications.[3] NeuroKit2 is presented as one of the most popular and contributor-friendly open-source software for neurophysiology based on the number of downloads, the number of contributors, and other GitHub metricsa.[4]

History

The first version of NeuroKit was created as a PhD side-project of Dominique Makowski in 2017.[1] It was officially deprecated in 2020 and has been replaced by the current version, NeuroKit2. A few major updates have been released since:[5]

  • February 08, 2021: The 0.1.0 release coincides with the first publication of the software.
  • May 18, 2022: The 0.2.0 release coincides with an overhaul of the documentation.


Features

NeuroKit2 includes tools to work with cardiac activity from electrocardiography (ECG) and photoplethysmography (PPG), electrodermal activity (EDA), respiratory (RSP), electromyography (EMG), and electrooculography (EOG) signals.[6]

It enables the computation of Heart Rate Variability (HRV) and Respiratory Variability (RRV) metrics.[7][8]

It also implements a variety of different algorithms to detect R-peaks and other QRS waves, including an efficient in-house R-peak detector.[9][10]

For neurophysiological signals such as EEG, it supports microstates and frequency band analysis.[citation needed]

It also includes a comprehensive set of functions used for fractal physiology, allowing the computation of various measures of complexity (including entropy and fractal dimensions).[11]

Design

The software was designed to be accessible to users without programming experience, with the possibility of using high-level functions to run entire preprocessing or analysis routines.[1][12] 

import neurokit2 as nk

# Download example data
data = nk.data("bio_eventrelated_100hz")

# Preprocess the data (filter, find peaks, etc.)
processed_data, info = nk.bio_process(ecg=data["ECG"], rsp=data["RSP"], eda=data["EDA"], sampling_rate=100)

# Compute relevant features
results = nk.bio_analyze(processed_data, sampling_rate=100)

See also

Other open-source toolboxes for analysis of physiological signals include:

Notes

^ As of May 18, 2022, GitHub indicates that the package has 644 stars, 47 contributors, and is used in 101 other open-source applications.[13]

References

  1. ↑ 1.0 1.1 1.2 Makowski, Dominique; Pham, Tam; Lau, Zen J.; Brammer, Jan C.; Lespinasse, François; Pham, Hung; Schölzel, Christopher; Chen, S. H. Annabel (August 2021). "NeuroKit2: A Python toolbox for neurophysiological signal processing". Behavior Research Methods 53 (4): 1689–1696. doi:10.3758/s13428-020-01516-y. PMID 33528817. 
  2. ↑ "neurokit2". https://pypi.org/project/neurokit2/. 
  3. ↑ "NeuroKit2 article - Statistics". https://www.researchgate.net/publication/348985040. 
  4. ↑ "NeuroKit2 - Popularity". February 2021. https://github.com/neuropsychology/NeuroKit#popularity. 
  5. ↑ "NeuroKit2 Versions" (in en). https://github.com/neuropsychology/NeuroKit/releases. 
  6. ↑ Jaber, Dalia; Hajj, Hazem; Maalouf, Fadi; El-Hajj, Wassim (December 2022). "Medically-oriented design for explainable AI for stress prediction from physiological measurements". BMC Medical Informatics and Decision Making 22 (1): 12. doi:10.1186/s12911-022-01772-2. PMID 35148762. 
  7. ↑ Pham, Tam; Lau, Zen Juen; Chen, S. H. Annabel; Makowski, Dominique (9 June 2021). "Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial". Sensors 21 (12): 3998. doi:10.3390/s21123998. PMID 34207927. Bibcode: 2021Senso..21.3998P. 
  8. ↑ Frasch, Martin G. (1 January 2022). "Comprehensive HRV estimation pipeline in Python using Neurokit2: Application to sleep physiology". MethodsX 9: 101782. doi:10.1016/j.mex.2022.101782. PMID 35880142. 
  9. ↑ Baraeinejad, Bardia; Fallah Shayan, Masood; Vazifeh, Amir Reza; Rashidi, Diba; Saberi Hamedani, Mohammad; Tavolinejad, Hamed; Gorji, Pouya; Razmara, Parsa et al. (December 2021). "Design and Implementation of an Ultra-Low-Power ECG Patch and Smart Cloud-Based Platform". TechRxiv: 5. doi:10.36227/techrxiv.17003401. https://figshare.com/articles/preprint/Design_and_Implementation_of_an_Ultra-Low-Power_ECG_Patch_and_Smart_Cloud-Based_Platform/17003401. 
  10. ↑ "R-peak detection benchmark" (in en). https://sleepecg.readthedocs.io/en/stable/heartbeat_detection.html#performance-evaluation. 
  11. ↑ Makowski, Dominique; Te, An Shu; Pham, Tam; Lau, Zen Juen; Chen, S. H. Annabel (27 July 2022). "The Structure of Chaos: An Empirical Comparison of Fractal Physiology Complexity Indices Using NeuroKit2". Entropy 24 (8): 1036. doi:10.3390/e24081036. PMID 36010700. Bibcode: 2022Entrp..24.1036M. 
  12. ↑ "Biosignal processing for automatic emotion recognition" (in en-us). https://school.brainhackmtl.org/project/biosignalemotions/. 
  13. ↑ "NeuroKit2 - Popularity". February 2021. https://github.com/neuropsychology/NeuroKit#popularity. 



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