neurokit2 0.0.28

The Python Toolbox for Neurophysiological Signal Processing.

The Python Toolbox for Neurophysiological Signal Processing

This package is the continuation of NeuroKit 1. It’s a user-friendly package providing easy access to advanced biosignal processing routines. Researchers and clinicians without extensive knowledge of programming or biomedical signal processing can analyze physiological data with only two lines of code.

Quick Example

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)

And boom 💥 your analysis is done 😎

Installation

To install NeuroKit2, run this command in your terminal:

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

If you’re not sure how/what to do, be sure to read our installation guide.

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. Once this is done, you can also read our ideas for first contributions

Documentation

Click on the links above and check out our tutorials:

Tutorials

• Recording good quality signals

• What software for physiological signal processing

• Install Python and NeuroKit

• Get familiar with Python in 10 minutes

• How to use GitHub to contribute

• Ideas for first contributions

• Understanding NeuroKit

• Included datasets

• Additional Resources

Examples

• Simulate Artificial Physiological Signals

• Customize your Processing Pipeline

• Event-related Analysis

• Interval-related Analysis

• Analyze Electrodermal Activity (EDA)

• Analyze Respiratory Rate Variability (RRV)

• Extract and Visualize Individual Heartbeats

• Locate P, Q, S and T waves in ECG

You can try out these examples directly in your browser.

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

Citation

nk.cite()

You can cite NeuroKit2 as follows:

- Makowski, D., Pham, T., Lau, Z. J., Brammer, J. C., Lesspinasse, F., Pham, H.,
  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 Lespinasse, Fran\c{c}ois and Pham, Hung and Schölzel, Christopher and S H Chen, Annabel},
  title = {NeuroKit2: A Python Toolbox for Neurophysiological Signal Processing},
  publisher = {Zenodo},
  year = {2020},
}

Preprocessing

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/GSR)

# 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
signal, info = 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!

Electrooculography (EOG)

Consider helping us develop it!

Analysis

The analysis of physiological data usually comes in two types, event-related or interval-related.

Event-related

This type of analysis refers to physiological changes immediately occurring in response to an event. For instance, physiological changes following the presentation of a stimulus (e.g., an emotional stimulus). In this situation the analysis is epoch-based. An epoch is a short chunk of the physiological signal (usually < 10 seconds), that is locked to a specific stimulus. In this case, using bio_analyze() will compute the following features:

• Features:

  • Rate changes (ECG, PPG, RSP): mean, minimum, maximum, minimum-time and maximum-time

  • Peak characteristics (EDA): peak presence, amplitude, rise time, peak-time

  • Phase characteristics (ECG, RSP): phase type (inspiration/expiration, systole/diastole), phase completion

• Event-related example

Interval-related

This type of analysis refers to the physiological characteristics and features that occur over longer periods of time (from a few seconds to days of activity). Typical use cases are either periods of resting-state, in which the activity is recorded for several minutes while the participant is at rest, or during different conditions in which there is no specific time-locked event (e.g., watching movies, listening to music, engaging in physical activity, etc.). For instance, this type of analysis is used when people want to compare the physiological activity under different intensities of physical exercise, different types of movies, or different intensities of stress. In this case, using bio_analyze() will compute the following features:

• Features:

  • Rate characteristics (ECG, PPG, RSP): mean, amplitude, variability (HRV, RRV)

  • Peak characteristics (EDA): number of peaks, mean amplitude

• Interval-related example

Popularity

Notes

The authors do not provide any warranty. If this software causes your keyboard to blow up, your brain to liquify, your toilet to clog or a zombie plague to break loose, the authors CANNOT IN ANY WAY be held responsible.

News

0.0.1 (2019-10-29)

• First release on PyPI.