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%matplotlib inline
This tutorial covers the basics of signal-space projection (SSP) and shows how SSP can be used for artifact repair; extended examples illustrate use of SSP for environmental noise reduction, and for repair of ocular and heartbeat artifacts. :depth: 2
We begin as always by importing the necessary Python modules. To save ourselves
from repeatedly typing mne.preprocessing we'll directly import a couple
functions from that submodule:
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import os
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.preprocessing import (create_eog_epochs, create_ecg_epochs,
compute_proj_ecg, compute_proj_eog)
Before applying SSP (or any artifact repair strategy), be sure to observe the artifacts in your data to make sure you choose the right repair tool. Sometimes the right tool is no tool at all — if the artifacts are small enough you may not even need to repair them to get good analysis results. See `tut-artifact-overview` for guidance on detecting and visualizing various types of artifact.
Signal-space projection (SSP) :footcite:UusitaloIlmoniemi1997 is a
technique for removing noise from EEG
and MEG signals by :term:projecting <projector> the signal onto a
lower-dimensional subspace. The subspace is chosen by calculating the average
pattern across sensors when the noise is present, treating that pattern as
a "direction" in the sensor space, and constructing the subspace to be
orthogonal to the noise direction (for a detailed walk-through of projection
see tut-projectors-background).
The most common use of SSP is to remove noise from MEG signals when the noise comes from environmental sources (sources outside the subject's body and the MEG system, such as the electromagnetic fields from nearby electrical equipment) and when that noise is stationary (doesn't change much over the duration of the recording). However, SSP can also be used to remove biological artifacts such as heartbeat (ECG) and eye movement (EOG) artifacts. Examples of each of these are given below.
The example data <sample-dataset> was recorded on a Neuromag system,
which stores SSP projectors for environmental noise removal in the system
configuration (so that reasonably clean raw data can be viewed in real-time
during acquisition). For this reason, all the :class:~mne.io.Raw data in
the example dataset already includes SSP projectors, which are noted in the
output when loading the data:
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sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
The example data <sample-dataset> also includes an "empty room"
recording taken the same day as the recording of the subject. This will
provide a more accurate estimate of environmental noise than the projectors
stored with the system (which are typically generated during annual
maintenance and tuning). Since we have this subject-specific empty-room
recording, we'll create our own projectors from it and discard the
system-provided SSP projectors (saving them first, for later comparison with
the custom ones):
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system_projs = raw.info['projs']
raw.del_proj()
empty_room_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'ernoise_raw.fif')
empty_room_raw = mne.io.read_raw_fif(empty_room_file)
Notice that the empty room recording itself has the system-provided SSP projectors in it — we'll remove those from the empty room file too.
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empty_room_raw.del_proj()
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for average in (False, True):
empty_room_raw.plot_psd(average=average, dB=False, xscale='log')
We create the SSP vectors using :func:~mne.compute_proj_raw, and control
the number of projectors with parameters n_grad and n_mag. Once
created, the field pattern of the projectors can be easily visualized with
:func:~mne.viz.plot_projs_topomap. We include the parameter
vlim='joint' so that the colormap is computed jointly for all projectors
of a given channel type; this makes it easier to compare their relative
smoothness. Note that for the function to know the types of channels in a
projector, you must also provide the corresponding :class:~mne.Info object:
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empty_room_projs = mne.compute_proj_raw(empty_room_raw, n_grad=3, n_mag=3)
mne.viz.plot_projs_topomap(empty_room_projs, colorbar=True, vlim='joint',
info=empty_room_raw.info)
Notice that the gradiometer-based projectors seem to reflect problems with individual sensor units rather than a global noise source (indeed, planar gradiometers are much less sensitive to distant sources). This is the reason that the system-provided noise projectors are computed only for magnetometers. Comparing the system-provided projectors to the subject-specific ones, we can see they are reasonably similar (though in a different order) and the left-right component seems to have changed polarity.
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fig, axs = plt.subplots(2, 3)
for idx, _projs in enumerate([system_projs, empty_room_projs[3:]]):
mne.viz.plot_projs_topomap(_projs, axes=axs[idx], colorbar=True,
vlim='joint', info=empty_room_raw.info)
We could visualize the different effects these have on the data by applying
each set of projectors to different copies of the :class:~mne.io.Raw object
using :meth:~mne.io.Raw.apply_proj. However, the :meth:~mne.io.Raw.plot
method has a proj parameter that allows us to temporarily apply
projectors while plotting, so we can use this to visualize the difference
without needing to copy the data. Because the projectors are so similar, we
need to zoom in pretty close on the data to see any differences:
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mags = mne.pick_types(raw.info, meg='mag')
for title, projs in [('system', system_projs),
('subject-specific', empty_room_projs[3:])]:
raw.add_proj(projs, remove_existing=True)
fig = raw.plot(proj=True, order=mags, duration=1, n_channels=2)
fig.subplots_adjust(top=0.9) # make room for title
fig.suptitle('{} projectors'.format(title), size='xx-large', weight='bold')
The effect is sometimes easier to see on averaged data. Here we use an
interactive feature of :func:mne.Evoked.plot_topomap to turn projectors on
and off to see the effect on the data. Of course, the interactivity won't
work on the tutorial website, but you can download the tutorial and try it
locally:
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events = mne.find_events(raw, stim_channel='STI 014')
event_id = {'auditory/left': 1}
# NOTE: appropriate rejection criteria are highly data-dependent
reject = dict(mag=4000e-15, # 4000 fT
grad=4000e-13, # 4000 fT/cm
eeg=150e-6, # 150 µV
eog=250e-6) # 250 µV
# time range where we expect to see the auditory N100: 50-150 ms post-stimulus
times = np.linspace(0.05, 0.15, 5)
epochs = mne.Epochs(raw, events, event_id, proj='delayed', reject=reject)
fig = epochs.average().plot_topomap(times, proj='interactive')
Plotting the ERP/F using evoked.plot() or evoked.plot_joint() with
and without projectors applied can also be informative.
As mentioned in the ICA tutorial <tut-artifact-ica>, an important
first step is visualizing the artifacts you want to repair. Here they are in
the raw data:
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# pick some channels that clearly show heartbeats and blinks
regexp = r'(MEG [12][45][123]1|EEG 00.)'
artifact_picks = mne.pick_channels_regexp(raw.ch_names, regexp=regexp)
raw.plot(order=artifact_picks, n_channels=len(artifact_picks))
MNE-Python provides several functions for detecting and removing heartbeats
from EEG and MEG data. As we saw in tut-artifact-overview,
:func:~mne.preprocessing.create_ecg_epochs can be used to both detect and
extract heartbeat artifacts into an :class:~mne.Epochs object, which can
be used to visualize how the heartbeat artifacts manifest across the sensors:
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ecg_evoked = create_ecg_epochs(raw).average()
ecg_evoked.plot_joint()
Looks like the EEG channels are pretty spread out; let's baseline-correct and plot again:
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ecg_evoked.apply_baseline((None, None))
ecg_evoked.plot_joint()
To compute SSP projectors for the heartbeat artifact, you can use
:func:~mne.preprocessing.compute_proj_ecg, which takes a
:class:~mne.io.Raw object as input and returns the requested number of
projectors for magnetometers, gradiometers, and EEG channels (default is two
projectors for each channel type).
:func:~mne.preprocessing.compute_proj_ecg also returns an :term:events
array containing the sample numbers corresponding to the onset of each
detected heartbeat.
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projs, events = compute_proj_ecg(raw, n_grad=1, n_mag=1, n_eeg=1, reject=None)
The first line of output tells us that
:func:~mne.preprocessing.compute_proj_ecg found three existing projectors
already in the :class:~mne.io.Raw object, and will include those in the
list of projectors that it returns (appending the new ECG projectors to the
end of the list). If you don't want that, you can change that behavior with
the boolean no_proj parameter. Since we've already run the computation,
we can just as easily separate out the ECG projectors by indexing the list of
projectors:
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ecg_projs = projs[3:]
print(ecg_projs)
Just like with the empty-room projectors, we can visualize the scalp distribution:
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mne.viz.plot_projs_topomap(ecg_projs, info=raw.info)
Since no dedicated ECG sensor channel was detected in the
:class:~mne.io.Raw object, by default
:func:~mne.preprocessing.compute_proj_ecg used the magnetometers to
estimate the ECG signal (as stated on the third line of output, above). You
can also supply the ch_name parameter to restrict which channel to use
for ECG artifact detection; this is most useful when you had an ECG sensor
but it is not labeled as such in the :class:~mne.io.Raw file.
The next few lines of the output describe the filter used to isolate ECG
events. The default settings are usually adequate, but the filter can be
customized via the parameters ecg_l_freq, ecg_h_freq, and
filter_length (see the documentation of
:func:~mne.preprocessing.compute_proj_ecg for details).
.. TODO what are the cases where you might need to customize the ECG filter? infants? Heart murmur?
Once the ECG events have been identified,
:func:~mne.preprocessing.compute_proj_ecg will also filter the data
channels before extracting epochs around each heartbeat, using the parameter
values given in l_freq, h_freq, filter_length, filter_method,
and iir_params. Here again, the default parameter values are usually
adequate.
.. TODO should advice for filtering here be the same as advice for filtering raw data generally? (e.g., keep high-pass very low to avoid peak shifts? what if your raw data is already filtered?)
By default, the filtered epochs will be averaged together
before the projection is computed; this can be controlled with the boolean
average parameter.
.. TODO what is the (dis)advantage of not averaging before projection?
To get a sense of how the heartbeat affects the signal at each sensor, you can plot the data with and without the ECG projectors:
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raw.del_proj()
for title, proj in [('Without', empty_room_projs), ('With', ecg_projs)]:
raw.add_proj(proj, remove_existing=False)
fig = raw.plot(order=artifact_picks, n_channels=len(artifact_picks))
fig.subplots_adjust(top=0.9) # make room for title
fig.suptitle('{} ECG projectors'.format(title), size='xx-large',
weight='bold')
Finally, note that above we passed reject=None to the
:func:~mne.preprocessing.compute_proj_ecg function, meaning that all
detected ECG epochs would be used when computing the projectors (regardless
of signal quality in the data sensors during those epochs). The default
behavior is to reject epochs based on signal amplitude: epochs with
peak-to-peak amplitudes exceeding 50 µV in EEG channels, 250 µV in EOG
channels, 2000 fT/cm in gradiometer channels, or 3000 fT in magnetometer
channels. You can change these thresholds by passing a dictionary with keys
eeg, eog, mag, and grad (though be sure to pass the threshold
values in volts, teslas, or teslas/meter). Generally, it is a good idea to
reject such epochs when computing the ECG projectors (since presumably the
high-amplitude fluctuations in the channels are noise, not reflective of
brain activity); passing reject=None above was done simply to avoid the
dozens of extra lines of output (enumerating which sensor(s) were responsible
for each rejected epoch) from cluttering up the tutorial.
:func:`~mne.preprocessing.compute_proj_ecg` has a similar parameter ``flat`` for specifying the *minimum* acceptable peak-to-peak amplitude for each channel type.
While :func:~mne.preprocessing.compute_proj_ecg conveniently combines
several operations into a single function, MNE-Python also provides functions
for performing each part of the process. Specifically:
:func:mne.preprocessing.find_ecg_events for detecting heartbeats in a
:class:~mne.io.Raw object and returning a corresponding :term:events
array
:func:mne.preprocessing.create_ecg_epochs for detecting heartbeats in a
:class:~mne.io.Raw object and returning an :class:~mne.Epochs object
:func:mne.compute_proj_epochs for creating projector(s) from any
:class:~mne.Epochs object
See the documentation of each function for further details.
Once again let's visualize our artifact before trying to repair it. We've seen above the large deflections in frontal EEG channels in the raw data; here is how the ocular artifacts manifests across all the sensors:
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eog_evoked = create_eog_epochs(raw).average()
eog_evoked.apply_baseline((None, None))
eog_evoked.plot_joint()
Just like we did with the heartbeat artifact, we can compute SSP projectors
for the ocular artifact using :func:~mne.preprocessing.compute_proj_eog,
which again takes a :class:~mne.io.Raw object as input and returns the
requested number of projectors for magnetometers, gradiometers, and EEG
channels (default is two projectors for each channel type). This time, we'll
pass no_proj parameter (so we get back only the new EOG projectors, not
also the existing projectors in the :class:~mne.io.Raw object), and we'll
ignore the events array by assigning it to _ (the conventional way of
handling unwanted return elements in Python).
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eog_projs, _ = compute_proj_eog(raw, n_grad=1, n_mag=1, n_eeg=1, reject=None,
no_proj=True)
Just like with the empty-room and ECG projectors, we can visualize the scalp distribution:
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mne.viz.plot_projs_topomap(eog_projs, info=raw.info)
Now we repeat the plot from above (with empty room and ECG projectors) and compare it to a plot with empty room, ECG, and EOG projectors, to see how well the ocular artifacts have been repaired:
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for title in ('Without', 'With'):
if title == 'With':
raw.add_proj(eog_projs)
fig = raw.plot(order=artifact_picks, n_channels=len(artifact_picks))
fig.subplots_adjust(top=0.9) # make room for title
fig.suptitle('{} EOG projectors'.format(title), size='xx-large',
weight='bold')
Notice that the small peaks in the first to magnetometer channels (MEG
1411 and MEG 1421) that occur at the same time as the large EEG
deflections have also been removed.
In the examples above, we used 3 projectors (all magnetometer) to capture empty room noise, and saw how projectors computed for the gradiometers failed to capture global patterns (and thus we discarded the gradiometer projectors). Then we computed 3 projectors (1 for each channel type) to capture the heartbeat artifact, and 3 more to capture the ocular artifact. How did we choose these numbers? The short answer is "based on experience" — knowing how heartbeat artifacts typically manifest across the sensor array allows us to recognize them when we see them, and recognize when additional projectors are capturing something else other than a heartbeat artifact (and thus may be removing brain signal and should be discarded).
.. footbibliography::