Migrating from other analysis software#

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

EEGLAB#

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	MNE-Python
Get started	`addpath(...);` `eeglab;`	`import mne` `from mne import io,` `Epochs` `from mne.preprocessing` `import ICA`
Import data	`EEG = pop_fileio(fname);`	`raw = io.read_raw_fif(fname)` `raw = io.read_raw_edf(fname)` `raw = io.read_raw_eeglab(fname)` `(set file)`
Filter data	`EEG = pop_eegfiltnew(EEG, l_freq, h_freq);`	`raw.filter(l_freq, h_freq)`
Common Average referencing	`EEG= pop_averef;`	`raw.set_eeg_reference("average")`
Remove channels	`pop_select.m`	`raw.drop_channels()`
Run ICA	`EEG = pop_runica(EEG, 'pca', n);` `EEG = pop_binica(EEG, 'pca', n);`	`ica.fit(raw)` `mne.preprocessing.infomax()`
Plot ICA properties	`pop_compprop( EEG, comp_num, winhandle);`	`ica.plot_properties(raw, picks)`
Plot ICA components	`compheads()`	`ica.plot_components(raw, picks)`
Exclude components	`pop_selectcomps()`	`ica.exclude = list_of_components_to_exclude`
Epoch data	`event_id = {'cond1', 'cond2'};` `Epochs = pop_epochs(EEG, event_id, [tmin, tmax]);`	`events = mne.find_events(raw)` `event_id = dict(cond1=32, cond2=64)` `epochs = Epochs(raw, events, event_id, tmin, tmax)`
Selecting epochs	`Epochs = pop_epochs(EEG_epochs, {cond2});`	`epochs[cond2]`
ERP butterfly plot	`pop_timtopo(EEG_epochs, ...);`	`evoked = epochs[cond2].average()` `evoked.plot()` `evoked.plot_joint()`
Contrast ERPs	`pop_compareerps(EEG_epochs1, EEG_epochs2);`	`mne.combine_evoked([evoked1, -evoked2], weights='equal').plot()` `mne.viz.plot_compare_evokeds([evoked1, evoked2])`
Save data	`EEG = pop_saveset(EEG, fname);`	`raw.save(fname)` `epochs.save(fname)` `evoked.save(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_types, epochs.pick_types, evoked.pick_types, etc.