neurokit2 0.0.15

The Python Toolbox for Neurophysiological Signal Processing.

The Python Toolbox for Neurophysiological Signal Processing (EDA, ECG, PPG, EMG, EEG…)

This is a work-in-progress project continuation of NeuroKit.py. We are looking to build a community around this collaborative project. If you’re interested in getting involved, let us know!

Installation

To install NeuroKit2, run this command in your terminal:

pip install https://github.com/neuropsychology/neurokit/zipball/master

Contribution

NeuroKit2 is a collaborative project for contributors with all levels of development expertise. Additionally, we credit all contributors, so that your involvement pays off on your CV. Thus, if you have some ideas for improvement, new features, or just want to learn Python and do something useful at the same time, do not hesitate and check out the CONTRIBUTION guide.

Documentation

Click on the links above and check out our tutorials:

Tutorials

• Install Python and NeuroKit

• How to contribute

• Understanding NeuroKit

Examples

• Extract and Visualize Individual Heartbeats

• Electrodermal Activity (EDA) Analysis

• Customize your Processing Pipeline

• Event-related Analysis

• Respiratory Rate Variability Analysis

• Delineate QRS peaks, onsets and offsets

Overview

Simulate physiological signals

import numpy as np
import pandas as pd
import neurokit2 as nk

# Generate synthetic signals
ecg = nk.ecg_simulate(duration=10, heart_rate=70)
rsp = nk.rsp_simulate(duration=10, respiratory_rate=15)
eda = nk.eda_simulate(duration=10, n_scr=3)
emg = nk.emg_simulate(duration=10, n_bursts=2)

# Visualise biosignals
data = pd.DataFrame({"ECG": ecg,
                     "RSP": rsp,
                     "EDA": eda,
                     "EMG": emg})
nk.signal_plot(data, subplots=True)

Electrodermal Activity (EDA)

# Generate 10 seconds of EDA signal (recorded at 250 samples / second) with 2 SCR peaks
eda = nk.eda_simulate(duration=10, sampling_rate=250, n_scr=2 drift=0.01)

# Process it
signals, info = nk.eda_process(eda, sampling_rate=250)

# Visualise the processing
nk.eda_plot(signals, sampling_rate=250)

Cardiac activity (ECG)

# Generate 15 seconds of ECG signal (recorded at 250 samples / second)
ecg = nk.ecg_simulate(duration=15, sampling_rate=250, heart_rate=70)

# Process it
signals, info = nk.ecg_process(ecg, sampling_rate=250)

# Visualise the processing
nk.ecg_plot(signals, sampling_rate=250)

Respiration (RSP)

# Generate one minute of respiratory (RSP) signal (recorded at 250 samples / second)
rsp = nk.rsp_simulate(duration=60, sampling_rate=250, respiratory_rate=15)

# Process it
signals, info = nk.rsp_process(rsp, sampling_rate=250)

# Visualise the processing
nk.rsp_plot(signals, sampling_rate=250)

Electromyography (EMG)

# Generate 10 seconds of EMG signal (recorded at 250 samples / second)
emg = nk.emg_simulate(duration=10, sampling_rate=250, n_bursts=3)

# Process it
signals = nk.emg_process(emg, sampling_rate=250)

# Visualise the processing
nk.emg_plot(signals, sampling_rate=250)

PPG, BVP, EGG, …

Consider helping us develop it!

Design

NeuroKit2 is designed to provide a consistent, accessible yet powerful and flexible API.

• Consistency: For each type of signals (ECG, RSP, EDA, EMG…), the same function names are called (in the form signaltype_functiongoal()) to achieve equivalent goals, such as *_clean(), *_findpeaks(), *_process(), *_plot() (replace the star with the signal type, e.g., ecg_clean()).

• Accessibility: Using NeuroKit2 is made very easy for beginners through the existence of powerful high-level “master” functions, such as *_process(), that performs cleaning, preprocessing and processing with sensible defaults.

• Flexibility: However, advanced users can very easily build their own custom analysis pipeline by using the mid-level functions (such as *_clean(), *_rate()), offering more control and flexibility over their parameters.

Citation

You can run:

print(nk.__cite__)

You can cite NeuroKit as follows:

- Makowski, D., Pham, T., Lau, Z. J., Brammer, J. C., Pham, H., Lesspinasse, F., Schölzel, C., & S H Chen, A. (2020). NeuroKit2: A Python Toolbox for Neurophysiological Signal Processing. Retrieved March 10, 2020, from https://github.com/neuropsychology/NeuroKit


Full bibtex reference:

@misc{neurokit,
  doi = {10.5281/ZENODO.3597887},
  url = {https://github.com/neuropsychology/NeuroKit},
  author = {Makowski, Dominique and Pham, Tam and Lau, Zen J. and Brammer, Jan C. and Pham, Hung and Lesspinasse, Fran\c{c}ois and Schölzel, Christopher and S H Chen, Annabel},
  title = {NeuroKit2: A Python Toolbox for Neurophysiological Signal Processing},
  publisher = {Zenodo},
  year = {2020},
}

Alternatives

Here’s a list of great alternative packages that you should check out:

General

• BioSPPy

• PySiology

• PsPM

• pyphysio

ECG

• biopeaks

• hrv

• hrv-analysis

• py-ecg-detectors

• HeartPy

• ECG_analysis

• pyedr

• Systole

EDA

• eda-explorer

• cvxEDA

• Pypsy

• BreatheEasyEDA (matlab)

• EDA (matlab)

EEG

• MNE

• unfold (matlab)

Eye-Tracking

• PyGaze

• PyTrack

News

0.0.1 (2019-10-29)

• First release on PyPI.