neurokit2 0.0.6

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 meant as a continuation of NeuroKit.py. We are looking to build a community of people around this collaborative project. If you’re interested by getting involved, do 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 meant to be a all-level-friendly collaborative project. Additionally, it tries to credit all contributors, so that your involvement pays off on your CV. Thus, if you have some ideas for improvement, new features, or just wanna 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:

• Intall Python and NeuroKit

• How to contribute

Examples

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})
data.plot(subplots=True)

Cardiac activity (ECG) processing

# Generate 20 seconds of ECG signal
ecg = nk.ecg_simulate(duration=20, heart_rate=70)

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

# Visualise the processing
nk.ecg_plot(signals)

Respiration (RSP) processing

# Generate one minute of respiratory (RSP) signal
rsp = nk.rsp_simulate(duration=60, respiratory_rate=15)

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

# Visualise the processing
nk.rsp_plot(signals)

Signal processing

Signal cleaning

• signal_distord(): Add noise of a given frequency, amplitude and shape to a signal.

• signal_binarize(): Convert a continuous signal into zeros and ones depending on a given threshold.

• signal_filter(): Filter a signal using ‘butterworth’, ‘fir’ or ‘savgol’ filters.

• signal_detrend(): Apply a baseline (order = 0), linear (order = 1), or polynomial (order > 1) detrending to the signal (i.e., removing a general trend).

• signal_smooth(): Signal smoothing using the convolution of a filter kernel.

• signal_psd(): Compute the Power Spectral Density (PSD).

Signal preprocessing

• signal_resample(): Up- or down-sample a signal.

• signal_interpolate(): Interpolate (fills the values between data points) a signal using different methods.

• signal_merge(): Arbitrary addition of two signals with different time ranges.

Signal processing

• signal_zerocrossings(): Locate the indices where the signal crosses zero.

• signal_findpeaks(): Locate peaks (local maxima) in a signal and their related characteristics, such as height (prominence), width and distance with other peaks.

• signal_plot(): Plot signal with events as vertical lines.

Other Utilities

Read data

• read_acqknowledge(): Read and format a BIOPAC’s AcqKnowledge file into a pandas’ dataframe.

Events (stimuli triggers and markers)

• events_find(): Find and select events in a continuous signal (e.g., from a photosensor).

• events_plot(): Plot events in signal.

• events_to_mne(): Create MNE compatible events for integration with M/EEG.

Design

NeuroKit2 is designed to provide a consistent, accessible yet 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 powerful high-level “master” functions, such as *_process(), that performs cleaning, preprocessing and processing, and 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.

Alternatives

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

General

• BioSPPy

• PySiology

• PsPM

• pyphysio

ECG

• hrv

• biopeaks

• py-ecg-detectors

• HeartPy

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.