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%matplotlib inline
This tutorial covers reducing environmental noise and compensating for head movement with SSS and Maxwell filtering. :depth: 2
As usual we'll start by importing the modules we need, loading some
example data <sample-dataset>, and cropping it to save on memory:
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import os
import mne
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, verbose=False)
raw.crop(tmax=60).load_data()
Signal-space separation (SSS) :footcite:TauluKajola2005,TauluSimola2006
is a technique based on the physics
of electromagnetic fields. SSS separates the measured signal into components
attributable to sources inside the measurement volume of the sensor array
(the internal components), and components attributable to sources outside
the measurement volume (the external components). The internal and external
components are linearly independent, so it is possible to simply discard the
external components to reduce environmental noise. Maxwell filtering is a
related procedure that omits the higher-order components of the internal
subspace, which are dominated by sensor noise. Typically, Maxwell filtering
and SSS are performed together (in MNE-Python they are implemented together
in a single function).
Like SSP <tut-artifact-ssp>, SSS is a form of projection. Whereas SSP
empirically determines a noise subspace based on data (empty-room recordings,
EOG or ECG activity, etc) and projects the measurements onto a subspace
orthogonal to the noise, SSS mathematically constructs the external and
internal subspaces from spherical harmonics_ and reconstructs the sensor
signals using only the internal subspace (i.e., does an oblique projection).
Maxwell filtering was originally developed for Elekta Neuromag® systems, and should be considered *experimental* for non-Neuromag data. See the Notes section of the :func:`~mne.preprocessing.maxwell_filter` docstring for details.
The MNE-Python implementation of SSS / Maxwell filtering currently provides the following features:
~mne.preprocessing.find_bad_channels_maxwell)mne.chpi.filter_chpi)TauluKajola2005 through :func:mne.epochs.average_movementsFor optimal use of SSS with data from Elekta Neuromag® systems, you should provide the path to the fine calibration file (which encodes site-specific information about sensor orientation and calibration) as well as a crosstalk compensation file (which reduces interference between Elekta's co-located magnetometer and paired gradiometer sensor units).
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fine_cal_file = os.path.join(sample_data_folder, 'SSS', 'sss_cal_mgh.dat')
crosstalk_file = os.path.join(sample_data_folder, 'SSS', 'ct_sparse_mgh.fif')
Before we perform SSS we'll look for bad channels — MEG 2443 is quite
noisy.
It is critical to mark bad channels in ``raw.info['bads']`` *before* calling :func:`~mne.preprocessing.maxwell_filter` in order to prevent bad channel noise from spreading.
Let's see if we can automatically detect it. To do this we need to
operate on a signal without line noise or cHPI signals, which is most
easily achieved using :func:mne.chpi.filter_chpi,
:func:mne.io.Raw.notch_filter, or :meth:mne.io.Raw.filter. For simplicity
we just low-pass filter these data:
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raw.info['bads'] = []
raw_check = raw.copy().pick_types(exclude=()).filter(None, 40)
auto_noisy_chs, auto_flat_chs = mne.preprocessing.find_bad_channels_maxwell(
raw_check, cross_talk=crosstalk_file, calibration=fine_cal_file,
verbose=True)
print(auto_noisy_chs) # we should find them!
print(auto_flat_chs) # none for this dataset
raw.info['bads'].extend(auto_noisy_chs + auto_flat_chs)
But this algorithm is not perfect. For example, it misses MEG 2313,
which has some flux jumps, because there are not enough flux jumps in the
recording. So it can still be useful to manually inspect and mark bad
channels:
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raw.info['bads'] += ['MEG 2313'] # from manual inspection
After that, performing SSS and Maxwell filtering is done with a
single call to :func:~mne.preprocessing.maxwell_filter, with the crosstalk
and fine calibration filenames provided (if available):
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raw_sss = mne.preprocessing.maxwell_filter(
raw, cross_talk=crosstalk_file, calibration=fine_cal_file, verbose=True)
To see the effect, we can plot the data before and after SSS / Maxwell filtering.
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raw.pick(['meg']).plot(duration=2, butterfly=True)
raw_sss.pick(['meg']).plot(duration=2, butterfly=True)
Notice that channels marked as "bad" have been effectively repaired by SSS,
eliminating the need to perform interpolation <tut-bad-channels>.
The heartbeat artifact has also been substantially reduced.
The :func:~mne.preprocessing.maxwell_filter function has parameters
int_order and ext_order for setting the order of the spherical
harmonic expansion of the interior and exterior components; the default
values are appropriate for most use cases. Additional parameters include
coord_frame and origin for controlling the coordinate frame ("head"
or "meg") and the origin of the sphere; the defaults are appropriate for most
studies that include digitization of the scalp surface / electrodes. See the
documentation of :func:~mne.preprocessing.maxwell_filter for details.
An assumption of SSS is that the measurement volume (the spherical shell
where the sensors are physically located) is free of electromagnetic sources.
The thickness of this source-free measurement shell should be 4-8 cm for SSS
to perform optimally. In practice, there may be sources falling within that
measurement volume; these can often be mitigated by using Spatiotemporal
Signal Space Separation (tSSS) :footcite:TauluSimola2006.
tSSS works by looking for temporal
correlation between components of the internal and external subspaces, and
projecting out any components that are common to the internal and external
subspaces. The projection is done in an analogous way to
SSP <tut-artifact-ssp>, except that the noise vector is computed
across time points instead of across sensors.
To use tSSS in MNE-Python, pass a time (in seconds) to the parameter
st_duration of :func:~mne.preprocessing.maxwell_filter. This will
determine the "chunk duration" over which to compute the temporal projection.
The chunk duration effectively acts as a high-pass filter with a cutoff
frequency of $\frac{1}{\mathtt{st\_duration}}~\mathrm{Hz}$; this
effective high-pass has an important consequence:
st_duration are better (provided that your
computer has sufficient memory) because larger values of st_duration
will have a smaller effect on the signal.If the chunk duration does not evenly divide your data length, the final (shorter) chunk will be added to the prior chunk before filtering, leading to slightly different effective filtering for the combined chunk (the effective cutoff frequency differing at most by a factor of 2). If you need to ensure identical processing of all analyzed chunks, either:
choose a chunk duration that evenly divides your data length (only recommended if analyzing a single subject or run), or
include at least 2 * st_duration of post-experiment recording time at
the end of the :class:~mne.io.Raw object, so that the data you intend to
further analyze is guaranteed not to be in the final or penultimate chunks.
Additional parameters affecting tSSS include st_correlation (to set the
correlation value above which correlated internal and external components
will be projected out) and st_only (to apply only the temporal projection
without also performing SSS and Maxwell filtering). See the docstring of
:func:~mne.preprocessing.maxwell_filter for details.
If you have information about subject head position relative to the sensors
(i.e., continuous head position indicator coils, or :term:cHPI <HPI>), SSS
can take that into account when projecting sensor data onto the internal
subspace. Head position data can be computed using
:func:mne.chpi.compute_chpi_locs and :func:mne.chpi.compute_head_pos,
or loaded with the:func:mne.chpi.read_head_pos function. The
example data <sample-dataset> doesn't include cHPI, so here we'll
load a :file:.pos file used for testing, just to demonstrate:
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head_pos_file = os.path.join(mne.datasets.testing.data_path(), 'SSS',
'test_move_anon_raw.pos')
head_pos = mne.chpi.read_head_pos(head_pos_file)
mne.viz.plot_head_positions(head_pos, mode='traces')
The cHPI data file could also be passed as the head_pos parameter of
:func:~mne.preprocessing.maxwell_filter. Not only would this account for
movement within a given recording session, but also would effectively
normalize head position across different measurement sessions and subjects.
See here <example-movement-comp> for an extended example of applying
movement compensation during Maxwell filtering / SSS. Another option is to
apply movement compensation when averaging epochs into an
:class:~mne.Evoked instance, using the :func:mne.epochs.average_movements
function.
Each of these approaches requires time-varying estimates of head position,
which is obtained from MaxFilter using the -headpos and -hp
arguments (see the MaxFilter manual for details).
There are patents related to the Maxwell filtering algorithm, which may
legally preclude using it in commercial applications. More details are
provided in the documentation of
:func:~mne.preprocessing.maxwell_filter.
SSS works best when both magnetometers and gradiometers are present, and is most effective when gradiometers are planar (due to the need for very accurate sensor geometry and fine calibration information). Thus its performance is dependent on the MEG system used to collect the data.
.. footbibliography::
.. LINKS