Migrating from EEGLAB¶

To read in data exported from EEGLAB, MNE offers 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().

Here is a cheatsheet to help users migrate painlessly from EEGLAB. For the sake of clarity, let us assume that the following are already defined or known: the file name fname, time interval of the epochs tmin and tmax, and the 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
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)`
Filter data	EEG = pop_eegfiltnew(EEG, l_freq, h_freq);	`raw.filter(l_freq, h_freq)`
Run ICA	EEG = pop_runica(EEG);	`ica.fit(raw)`
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.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)`

Note that MNE has functions to read a variety of file formats, not just mne.io.Raw(). The interested user is directed to the IO documentation.

Pitfalls¶

Python methods often operate in-place. This means the object the method is called on is modified in-place (e.g., see the filter example above). This can be confusing to new users migrating from Matlab. However, it is also possible to ask MNE functions not to modify the input. To do this, call the copy method of the object (.e.g, use raw_filtered = raw.copy().filter(l_freq, h_freq)).
The concept of channel types is critical in MNE because it supports analysis of multimodal data (e.g., EEG, MEG, EOG, Stim channel) whereas most EEGLAB functions assume the same channel type (EEG).