# EEGLAB and Python

EEGLAB does not work natively in Python because EEGLAB runs on MATLAB (and, to a considerable extent, on the open source Octave platform). Nevertheless, there are possible links with Python, which we are detailing here.

## Should I use MATLAB-based tools or Python-based tools

One of the most important feature when using a software package is usage and community. If the community is large and the software is popular, it is a safer choice as this ensures many problems people encounter have been solved - it also means that the code is probably more stable and has fewer bugs.

As of 2020, 56% of the citations of the papers below go to EEGLAB, then 25% go to Fieldtrip, and 19% go to Brainstorm and various versions of MNE. Note that EEGLAB and Fieldtrip are intertwined where Fieldtrip users can write EEGLAB plugins by adding simple wrappers on their Fieldtrip code. So the pair EEGLAB+Fieldtrip comprises 81% of the citations, and it is continuing to grow, with the MATLAB-based tools (which include Brainstorm) gathering about 90% of all citations. This is a strong argument for using MATLAB based tools - and in particular EEGLAB - instead of Python-based tools (i.e., MNE).

Below is an analysis of papers referencing EEGLAB, FieldTrip, MNE, MNE-Python, and Brainstorm since 2004. Data were obtained from Google Scholar.

The number of citation per year corresponds to the following five papers:

**EEGLAB**: Delorme, A. and Makeig, S., 2004. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods, 134(1), pp.9-21**Fieldtrip**: Oostenveld, R., Fries, P., Maris, E., Schoffelen, JM (2011). FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data. Computational Intelligence and Neuroscience, Volume 2011 (2011)**MNE 1**: A. Gramfort, M. Luessi, E. Larson, D. Engemann, D. Strohmeier, C. Brodbeck, L. Parkkonen, M. Hämäläinen, MNE software for processing MEG and EEG data, NeuroImage, Volume 86, 1 February 2014, Pages 446-460, ISSN 1053-8119,**MNE Python**: A. Gramfort, M. Luessi, E. Larson, D. Engemann, D. Strohmeier, C. Brodbeck, R. Goj, M. Jas, T. Brooks, L. Parkkonen, M. Hämäläinen, MEG and EEG data analysis with MNE-Python, Frontiers in Neuroscience, Volume 7, 2013, ISSN 1662-453X**Brainstorm**: Tadel, F., Baillet, S., Mosher, J.C., Pantazis, D. and Leahy, R.M., 2011. Brainstorm: a user-friendly application for MEG/EEG analysis. Computational intelligence and neuroscience, 2011, p.8.

## Major differences between MATLAB and Python

There is a trend in imaging tool development to migrate brain imaging tools to Python. Of course, Python (and the numpy/scipy math packages built on Python) would be an interesting (and free) alternative to using MATLAB. However, irrespective of what Python enthusiasts might claim, Python might not be ideal because it remains a programming language designed for programmers. For example,

- It is hard to understand for novices why an n-size vector should be indexed, beginning at 0 and ending at n-1 (in MATLAB and R, vectors begin at position 1 and end at n).
- Code indentation is a nice feature of Python. However, this style does not come naturally to the novice programmer. It also makes copying and pasting code between file sources and the command line interface problematic (since a snippet of code will most likely have unwanted indentation when copied to the Python command line).
- Python is much more object-oriented than MATLAB, sometimes requiring users to understand object-oriented concepts when calling functions.
- Python usually requires the user to install multiple external libraries; this can be tedious and does not come naturally to novices. Even experienced users sometimes spend hours getting their library settings right. There are also other technical problems related to the operating system and library compatibility that can take hours or days to solve (we speak from experience).
- Matrix manipulation in Python is not as intuitive as MATLAB. For example, the already non-intuitive Python code to concatenate arrays
*np.concatenate((np.array([[/1,*will fail because, unlike MATLAB, 1-D vectors are not compatible with 2-D matrices by default - and need explicit conversion. Compare to MATLAB simpler notation*2],*[5,_6|1, 2], [5, 6]]), np.array([1, 2])))*[ [1 2; 5 6]; [1 2] ]*or*[ [1 2; 5 6] [1 2]’ ]*depending on the dimension to concatenate. The MATLAB code is readable for someone with math training. - And of course, version problems: Python versions 2 and 3 are not fully compatible – and Python 2.7, although no longer supported since January 1, 2020, is still widely used because a large number of Python libraries are not available in Python 3 – leading to all kinds of unexpected problems that can slow down a novice programmer.
- Python is free. Why should I have to pay for MATLAB? Good conduct in (open) science should transcend discussions on finances. We pay for Microsoft or Adobe licenses because the free alternative, even if it exists, does not fulfill our needs. The compiled version of EEGLAB does not require users to purchase MATLAB, and EEGLAB code also runs on Octave.
- MEEG software packages on MATLAB are mainly EEGLAB, Fieldtrip, and Brainstorm. MEEG software on Python is MNE which is more tailored to MEG users than EEG users. The MATLAB suite of available software is currently more mature than the Python one, which is a good reason to stick to MATLAB.
- The closest alternative to the Matlab interactive interface is the Jupyter notebook environment that runs in your browser. However, the graphical capabilities of Jupyter notebooks remain limited (it is sometimes hard to manipulate figures, impossible to zoom, etc…). Most people who are used to Matlab and tried Jupyter notebooks dislike Jupyter notebooks - then learn to live with the limitations if they need it for their work. By contrast, the less popular Spyder IDE is a decent equivalent of the MATLAB graphical interface and should feel more familiar.

## How to call EEGLAB functions from Python

If you want or need to call EEGLAB functions from Python, the best solution is to use the Python package Oct2py (pip install Oct2py). You will need to install Octave as well. See this page for more information on how to run EEGLAB on Octave. The way this Python library works is that it converts Python data structures to MATLAB/Octave data structures and vice versa. Based on our research, it is the simplest and most stable way to run MATLAB functions in Python, and most EEGLAB functions may be called from within Python using this method.

```
# import dataset from EEGLAB
from oct2py import octave
octave.addpath('/data/matlab/eeglab/functions/guifunc');
octave.addpath('/data/matlab/eeglab/functions/popfunc');
octave.addpath('/data/matlab/eeglab/functions/adminfunc');
octave.addpath('/data/matlab/eeglab/functions/sigprocfunc');
octave.addpath('/data/matlab/eeglab/functions/miscfunc');
EEG = octave.pop_loadset('/data/matlab/eeglab/sample_data/eeglab_data_epochs_ica.set');
# plot first trial of channel 1
import matplotlib.pyplot as plt
plt.plot(EEG.data[0][0]);
plt.show()
```

The SCIPY Python library can import EEGLAB files, when the raw data is embedded in the *.set* file.

```
import scipy.io as sio
EEG = sio.loadmat('eeglabfile.set')
```

If the raw data is stored in a separate *.fdt* file, the *read_epochs_eeglab* MNE function can also import EEGLAB data files.

```
import mne
EEG = mne.io.read_epochs_eeglab('eeglabfile.set')
```

## Will EEGLAB ever run natively on Python?

For the foreseeable future, MATLAB will remain the platform on, which EEGLAB is developed and supported. MATLAB has a breadth of useful tools that are not yet matched by open source environments (e.g., no complex system to install libraries, good graphical support for different platforms, 3-D interactive graphics with transparency, powerful debugging tools, capacity to run native Java code), plus a wealth of available MATLAB toolboxes are handy, well known and well tested (e.g., image processing toolbox, for correcting for multiple comparisons; signal processing toolbox, for spectral decompositions; optimization toolbox, for optimizing code; bioinformatics toolbox, useful for EEG classification; virtual reality toolbox, for the real-time 3-D rendering of EEG activity). Finally, the MATLAB compiler allows us to create a compiled version of EEGLAB that does not require the user to have MATLAB – MATLAB scripts can be run by compiled EEGLAB, although interactive sessions are not supported.