Migrating from other analysis software#

Here we offer some tips on how to migrate from other analysis software.


To read in data exported from EEGLAB, MNE-Python includes an .edf reader mne.io.read_raw_edf() and a set file reader. To read in set files containing raw data, use mne.io.read_raw_eeglab() and to read in set files containing epochs data, use mne.read_epochs_eeglab().

This table summarizes the equivalent EEGLAB and MNE-Python code for some of the most common analysis tasks. For the sake of clarity, the table below assumes the following variables exist: the file name fname, time interval of the epochs tmin and tmax, and the experimental conditions cond1 and cond2. The variables l_freq and h_freq are the frequencies (in Hz) below which and above which to filter out data.

Processing step

EEGLAB function


Get started


Import data

EEG = pop_fileio(fname);

Filter data

EEG = pop_eegfiltnew(EEG, l_freq, h_freq);

Common Average referencing

EEG= pop_averef;

Remove channels



EEG = pop_runica(EEG, 'pca', n);

EEG = pop_binica(EEG, 'pca', n);

Plot ICA properties

pop_compprop( EEG, comp_num, winhandle);

Plot ICA components


Exclude components

ica.exclude = list_of_components_to_exclude

Epoch data

event_id = {'cond1', 'cond2'};
Epochs = pop_epochs(EEG, event_id, [tmin, tmax]);

Selecting epochs

Epochs = pop_epochs(EEG_epochs, {cond2});

ERP butterfly plot

pop_timtopo(EEG_epochs, ...);

Contrast ERPs

pop_compareerps(EEG_epochs1, EEG_epochs2);

Save data

EEG = pop_saveset(EEG, fname);

Potential pitfalls#

  • Many of the MNE-Python objects have methods that operate in-place to save memory (i.e., the data in the Raw object is changed when you call raw.filter(lfreq, hfreq)). If you do not want this, it is always possible to first call the object’s copy() method (e.g., filtered_raw = raw.copy().filter(lfreq, hfreq)). In addition, some MNE-Python functions have a boolean copy parameter that achieves the same purpose.

  • The concept of channel types is critical in MNE because it supports analysis of multimodal data (e.g., EEG, MEG, EOG, Stim channel, etc) whereas most EEGLAB functions assume all channels are of the same type (EEG). To restrict channels to a single type, see mne.pick_types(), raw.pick(), epochs.pick() and evoked.pick().