migrating.rst

Migrating from other analysis software

.. tags:: help, migrating, interoperability

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 :file:`.edf` reader :func:`mne.io.read_raw_edf` and a set file reader. To read in set files containing raw data, use :func:`mne.io.read_raw_eeglab` and to read in set files containing epochs data, use :func:`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.

.. cssclass:: table-bordered

.. rst-class:: midvalign

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

Potential pitfalls

• Many of the MNE-Python objects have methods that operate in-place to save memory (i.e., the data in the :class:`~mne.io.Raw` object is changed when you call :meth:`raw.filter(lfreq, hfreq) <mne.io.Raw.filter>`). If you do not want this, it is always possible to first call the object's :meth:`~mne.io.Raw.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 :func:`mne.pick_types`, :meth:`raw.pick() <mne.io.Raw.pick>`, :meth:`epochs.pick() <mne.Epochs.pick>` and :meth:`evoked.pick() <mne.Evoked.pick>`.