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A Python Toolbox for Statistics and Signal Processing (EEG, EDA, ECG, EMG...).

Project description

<p align="center"><a href=><img src="" width="400" align="center" alt="neurokit python eeg biosignals meg electrophysiology logo"></a></p>

<h2 align="center">Neuroscience made easy!</h2>

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A Python Toolbox for Statistics and Neurophysiological Signal Processing (EEG, EDA, ECG, EMG...).

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## Installation

Run the following:

pip install

Not working? [Check this out](!

## Contribute

Want to get involved in the developpment of an open-source software and improve neuroscience practice? **Join us!**

- You need some help? You found a bug? You would like to request a new feature?
Just open an [issue]( :relaxed:
- Want to add a feature? Correct a bug? You're more than welcome to contribute!
Check [this page]( to see how to submit your changes on github.

## Documentation

- [Tutorials](
- [x] Biosignals processing
- [ ] M/EEG processing
- [API Documentation](

## Features

This package provides a high level integration of complex statistical routines for researchers and clinicians with not much experience in programming, statistics or signal theory.

- **[M/EEG](** *(under developpment)*
- **[`eeg_erp()`](**: Extract event-related potentials
- **[`eeg_complexity()`](**: Compute entropy, fractal dimension, and complexity indices
- Time/Frequency: **SOONâ„¢**
- Microstates: **SOONâ„¢**
- **[Biosignals](**
- **[`read_acqknowledge()`](**: Load and convert Biopac:copyright:'s AcqKnowledge files to a dataframe
- **[`ecg_process()`](**: Extract ECG features
- *Heart Rate*
- *Heart rate variability (HRV) - time, frequency and nonlinear domains*
- *Cardiac Cycles - R peaks, RR intervals, P, Q, T Waves, ...*
- *Cardiac Phase (systole/diastole)*
- *Signal quality evaluation*
- *Respiratory sinus arrhythmia (RSA) - P2T method*
- *Complexity (multiscale entropy, fractal dimensions, ...)*
- **[`rsp_process()`](**: Extract Respiratory features
- *Respiratory rate and variability*
- *Respiratory phase (inspiration/expiration)*
- *Respiratory cycles characteristics (onsets, length, ...)*
- **[`eda_process()`](**: Extract Electrodermal Activity (EDA)
- *Tonic and phasic components using the new cvxEDA algorithm ([Greco, 2016](*
- *Skin Conductance Responses (SCR) onsets, peaks, amplitudes, latencies, recovery times, ...*
- **[`emg_process()`](**: Extract EMG features
- *Pulse onsets*
- *Linear envelope, muscle activation*
- **Signal**
- **[`complexity()`](**: Extract complexity/chaos indices, such as values of entropy (Shannon's, Sample and Multiscale), fractal dimension, Hurst and Lyapunov exponents and more
- **Statistics**
- **[`z_score()`](**: Normalize (scale and reduce) variables
- **[`find_outliers()`](**: Identify outliers
- **Miscellaneous**
- **[`compute_dprime()`](**: Computes Signal Detection Theory (SDT) parameters (d', c, beta, a', b''d)
- **[`compute_BMI()`](**: Compute the traditional body mass index (BMI), the new BMI, the Body Fat Percentage (BFP) and their interpretation
- **[`compute_interoceptive_accuracy()`](**: Compute interoception accuracy according to Garfinkel et al., (2015).

## Citation
You can cite NeuroKit with the following:
Makowski, D. (2016). NeuroKit: A Python Toolbox for Statistics and Neurophysiological Signal Processing (EEG, EDA, ECG, EMG...).
Memory and Cognition Lab' Day, 01 November, Paris, France
*Note: The authors do not give any warranty. If this software causes your keyboard to blow up, your brain to liquefy, your toilet to clog or a zombie plague to leak, the authors CANNOT IN ANY WAY be held responsible.*

## Credits
Note that important credits go to the developpers of the many packages upon which NeuroKit is built. Those include [mne](, [bioSPPy](, [hrv](, [bioread]( Make sure you cite them!

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