neurokit2 0.0.18

The Python Toolbox for Neurophysiological Signal Processing.

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

This package is the continuation of NeuroKit1. It’s a user-friendly package with which you can analyze your physiological data with only two lines of code.

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 with a community of contributors with all levels of development expertise. 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

• Included datasets

Examples

• Simulate Artificial Physiological Signals

• Customize your Processing Pipeline

• Event-related Analysis

• Analyze Electrodermal Activity (EDA)

• Analyze Respiratory Rate Variability (RRV)

• Extract and Visualize Individual Heartbeats

• Delineate QRS peaks, onsets and offsets

You can try out these examples directly in your browser by clicking here.

Don’t know which tutorial is suited for your case? Follow this flowchart:

Citation

You can run:

nk.cite()

You can cite NeuroKit2 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 28, 2020, from https://github.com/neuropsychology/NeuroKit

Full bibtex reference:

@misc{neurokit2,
  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 Lespinasse, 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},
}

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)
ppg = nk.ppg_simulate(duration=10, heart_rate=70)
rsp = nk.rsp_simulate(duration=10, respiratory_rate=15)
eda = nk.eda_simulate(duration=10, scr_number=3)
emg = nk.emg_simulate(duration=10, burst_number=2)

# Visualise biosignals
data = pd.DataFrame({"ECG": ecg,
                     "PPG": ppg,
                     "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, scr_number=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, burst_number=3)

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

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

Photoplethysmography (PPG/BVP)

# Generate 15 seconds of PPG signal (recorded at 250 samples / second)
ppg = nk.ppg_simulate(duration=15, sampling_rate=250, heart_rate=70)

Electrogastrography (EGG)

Consider helping us develop it!

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.