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%matplotlib inline
MNE-Python reimplements most of MNE-C's (the original MNE command line utils) functionality and offers transparent scripting. On top of that it extends MNE-C's functionality considerably (customize events, compute contrasts, group statistics, time-frequency analysis, EEG-sensor space analyses, etc.) It uses the same files as standard MNE unix commands: no need to convert your files to a new system or database.
This package is based on the FIF file format from Neuromag. It can read and convert CTF, BTI/4D, KIT and various EEG formats to FIF.
ch_browse)ch_forward)See install_python_and_mne_python.
Now, launch ipython_ (Advanced Python shell) using the QT backend, which
is best supported across systems:
.. code-block:: console
$ ipython --matplotlib=qt
In IPython, you can press **shift-enter** with a given cell selected to execute it and advance to the next cell. Also, the standard location for the MNE-sample data is ``~/mne_data``. If you downloaded data and an example asks you whether to download it again, make sure the data reside in the examples directory and you run the script from its current directory. From IPython e.g. say: .. code-block:: IPython In [1]: cd examples/preprocessing In [2]: %run plot_find_ecg_artifacts.py
First, load the mne package:
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import mne
If you'd like to turn information status messages off:
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mne.set_log_level('WARNING')
But it's generally a good idea to leave them on:
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mne.set_log_level('INFO')
You can set the default level in every session by setting the environment variable "MNE_LOGGING_LEVEL", or by having mne-python write preferences to a file with::
>>> mne.set_config('MNE_LOGGING_LEVEL', 'WARNING')
Note that the location of the mne-python preferences file (for easier manual editing) can be found using:
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print(mne.get_config_path())
By default logging messages print to the console, but look at
:func:mne.set_log_file to save output to a file.
Access raw data ^^^^^^^^^^^^^^^
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from mne.datasets import sample # noqa
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
print(raw_fname)
The MNE sample dataset should be downloaded automatically but be patient (approx. 2GB)
Read data from file:
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raw = mne.io.read_raw_fif(raw_fname)
print(raw)
print(raw.info)
Look at the channels in raw:
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print(raw.ch_names)
Read and plot a segment of raw data
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start, stop = raw.time_as_index([100, 115]) # 100 s to 115 s data segment
data, times = raw[:, start:stop]
print(data.shape)
print(times.shape)
data, times = raw[2:20:3, start:stop] # access underlying data
raw.plot()
Save a segment of 150s of raw data (MEG only):
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picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=True,
exclude='bads')
raw.save('sample_audvis_meg_raw.fif', tmin=0, tmax=150, picks=picks,
overwrite=True)
Define and read epochs ^^^^^^^^^^^^^^^^^^^^^^
First extract events:
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events = mne.find_events(raw, stim_channel='STI 014')
print(events[:5])
Note that, by default, we use stim_channel='STI 014'. If you have a different system (e.g., a newer system that uses channel 'STI101' by default), you can use the following to set the default stim channel to use for finding events:
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mne.set_config('MNE_STIM_CHANNEL', 'STI101', set_env=True)
Events are stored as a 2D numpy array where the first column is the time instant and the last one is the event number. It is therefore easy to manipulate.
Define epochs parameters:
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event_id = dict(aud_l=1, aud_r=2) # event trigger and conditions
tmin = -0.2 # start of each epoch (200ms before the trigger)
tmax = 0.5 # end of each epoch (500ms after the trigger)
Exclude some channels (original bads + 2 more):
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raw.info['bads'] += ['MEG 2443', 'EEG 053']
The variable raw.info['bads'] is just a python list.
Pick the good channels, excluding raw.info['bads']:
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picks = mne.pick_types(raw.info, meg=True, eeg=True, eog=True, stim=False,
exclude='bads')
Alternatively one can restrict to magnetometers or gradiometers with:
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mag_picks = mne.pick_types(raw.info, meg='mag', eog=True, exclude='bads')
grad_picks = mne.pick_types(raw.info, meg='grad', eog=True, exclude='bads')
Define the baseline period:
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baseline = (None, 0) # means from the first instant to t = 0
Define peak-to-peak rejection parameters for gradiometers, magnetometers and EOG:
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reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)
Read epochs:
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epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks,
baseline=baseline, preload=False, reject=reject)
print(epochs)
Get single epochs for one condition:
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epochs_data = epochs['aud_l'].get_data()
print(epochs_data.shape)
epochs_data is a 3D array of dimension (55 epochs, 365 channels, 106 time instants).
Scipy supports read and write of matlab files. You can save your single trials with:
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from scipy import io # noqa
io.savemat('epochs_data.mat', dict(epochs_data=epochs_data), oned_as='row')
or if you want to keep all the information about the data you can save your epochs in a fif file:
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epochs.save('sample-epo.fif')
and read them later with:
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saved_epochs = mne.read_epochs('sample-epo.fif')
Compute evoked responses for auditory responses by averaging and plot it:
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evoked = epochs['aud_l'].average()
print(evoked)
evoked.plot(time_unit='s')
.. topic:: Exercise
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max_in_each_epoch = [e.max() for e in epochs['aud_l']] # doctest:+ELLIPSIS
print(max_in_each_epoch[:4]) # doctest:+ELLIPSIS
It is also possible to read evoked data stored in a fif file:
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evoked_fname = data_path + '/MEG/sample/sample_audvis-ave.fif'
evoked1 = mne.read_evokeds(
evoked_fname, condition='Left Auditory', baseline=(None, 0), proj=True)
Or another one stored in the same file:
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evoked2 = mne.read_evokeds(
evoked_fname, condition='Right Auditory', baseline=(None, 0), proj=True)
Two evoked objects can be contrasted using :func:mne.combine_evoked.
This function can use weights='equal', which provides a simple
element-by-element subtraction (and sets the
mne.Evoked.nave attribute properly based on the underlying number
of trials) using either equivalent call:
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contrast = mne.combine_evoked([evoked1, evoked2], weights=[0.5, -0.5])
contrast = mne.combine_evoked([evoked1, -evoked2], weights='equal')
print(contrast)
To do a weighted sum based on the number of averages, which will give
you what you would have gotten from pooling all trials together in
:class:mne.Epochs before creating the :class:mne.Evoked instance,
you can use weights='nave':
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average = mne.combine_evoked([evoked1, evoked2], weights='nave')
print(contrast)
Instead of dealing with mismatches in the number of averages, we can use
trial-count equalization before computing a contrast, which can have some
benefits in inverse imaging (note that here weights='nave' will
give the same result as weights='equal'):
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epochs_eq = epochs.copy().equalize_event_counts(['aud_l', 'aud_r'])[0]
evoked1, evoked2 = epochs_eq['aud_l'].average(), epochs_eq['aud_r'].average()
print(evoked1)
print(evoked2)
contrast = mne.combine_evoked([evoked1, -evoked2], weights='equal')
print(contrast)
Time-Frequency: Induced power and inter trial coherence ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Define parameters:
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import numpy as np # noqa
n_cycles = 2 # number of cycles in Morlet wavelet
freqs = np.arange(7, 30, 3) # frequencies of interest
Compute induced power and phase-locking values and plot gradiometers:
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from mne.time_frequency import tfr_morlet # noqa
power, itc = tfr_morlet(epochs, freqs=freqs, n_cycles=n_cycles,
return_itc=True, decim=3, n_jobs=1)
power.plot([power.ch_names.index('MEG 1332')])
Inverse modeling: MNE and dSPM on evoked and raw data ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Import the required functions:
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from mne.minimum_norm import apply_inverse, read_inverse_operator # noqa
Read the inverse operator:
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fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
inverse_operator = read_inverse_operator(fname_inv)
Define the inverse parameters:
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snr = 3.0
lambda2 = 1.0 / snr ** 2
method = "dSPM"
Compute the inverse solution:
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stc = apply_inverse(evoked, inverse_operator, lambda2, method)
Save the source time courses to disk:
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stc.save('mne_dSPM_inverse')
Now, let's compute dSPM on a raw file within a label:
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fname_label = data_path + '/MEG/sample/labels/Aud-lh.label'
label = mne.read_label(fname_label)
Compute inverse solution during the first 15s:
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from mne.minimum_norm import apply_inverse_raw # noqa
start, stop = raw.time_as_index([0, 15]) # read the first 15s of data
stc = apply_inverse_raw(raw, inverse_operator, lambda2, method, label,
start, stop)
Save result in stc files:
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stc.save('mne_dSPM_raw_inverse_Aud')
What else can you do? ^^^^^^^^^^^^^^^^^^^^^
- detect heart beat QRS component
- detect eye blinks and EOG artifacts
- compute SSP projections to remove ECG or EOG artifacts
- compute Independent Component Analysis (ICA) to remove artifacts or
select latent sources
- estimate noise covariance matrix from Raw and Epochs
- visualize cross-trial response dynamics using epochs images
- compute forward solutions
- estimate power in the source space
- estimate connectivity in sensor and source space
- morph stc from one brain to another for group studies
- compute mass univariate statistics base on custom contrasts
- visualize source estimates
- export raw, epochs, and evoked data to other python data analysis
libraries e.g. pandas
- and many more things ...
Want to know more ? ^^^^^^^^^^^^^^^^^^^
Browse the examples gallery <auto_examples/index.html>_.
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print("Done!")