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 metrics^a.^[4]

NeuroKit

Written in	Python
Operating system	All OS supported by Python
Available in	English
Type	Statistical software
License	MIT License
Website	github.com/neuropsychology/NeuroKit

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was created as a PhD side-project of Dominique Makowski in 2017.^[1] It was officially deprecated in 2020 and has been replaced by the current version, NeuroKit2.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.

NeuroKit has received the 2024 Commendation Award from the Society for the Improvement of Psychological Science (SIPS).^[6]

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.^[7]

It enables the computation of Heart Rate Variability (HRV) and Respiratory Variability (RRV) metrics.^[8][9]

It also implements a variety of different algorithms to detect R-peaks and other QRS waves, including an efficient in-house R-peak detector.^[10][11]

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).^[12]

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][13]

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

• Free and open-source software portal

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

• Neurophysiological Biomarker Toolbox (MatLab)
• EEGLAB (MatLab)
• MNE-Python (Python)

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.^[14]

References

1. 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". PyPI. Retrieved 23 March 2022.
3. "NeuroKit2 article - Statistics". ResearchGate. Retrieved 23 March 2022.
4. "NeuroKit2 - Popularity". GitHub. February 2021. Retrieved 23 March 2022.
5. "NeuroKit2 Versions". GitHub. Retrieved 18 August 2022.
6. "SIPS 2024 Awards". 24 July 2018. Retrieved 20 August 2024.
7. 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. PMC 8840288. PMID 35148762.
8. 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. Bibcode:2021Senso..21.3998P. doi:10.3390/s21123998. PMC 8230044. PMID 34207927.
9. 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. PMC 9307944. PMID 35880142.
10. Baraeinejad, Bardia; Fallah Shayan, Masood; Vazifeh, Amir Reza; Rashidi, Diba; Saberi Hamedani, Mohammad; Tavolinejad, Hamed; Gorji, Pouya; Razmara, Parsa; Vaziri, Kiarash; Vashaee, Daryoosh; Fakharzadeh, Mohammad (December 2021). "Design and Implementation of an Ultra-Low-Power ECG Patch and Smart Cloud-Based Platform". TechRxiv: 5. doi:10.36227/techrxiv.17003401. S2CID 244360958.
11. "R-peak detection benchmark". sleepecg.readthedocs.io. Retrieved 31 March 2022.
12. 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. Bibcode:2022Entrp..24.1036M. doi:10.3390/e24081036. PMC 9407071. PMID 36010700.
13. "Biosignal processing for automatic emotion recognition". BrainHack School. Retrieved 18 May 2022.
14. "NeuroKit2 - Popularity". GitHub. February 2021. Retrieved 23 March 2022.