Artifact Correction with SSP

import numpy as np

import mne
from mne.datasets import sample
from mne.preprocessing import compute_proj_ecg, compute_proj_eog

# getting some data ready
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'

raw = mne.io.read_raw_fif(raw_fname, preload=True)

Compute SSP projections

First let’s do ECG.

projs, events = compute_proj_ecg(raw, n_grad=1, n_mag=1, n_eeg=0, average=True)
print(projs)

ecg_projs = projs[-2:]
mne.viz.plot_projs_topomap(ecg_projs)
../_images/sphx_glr_plot_artifacts_correction_ssp_001.png

Out:

[<Projection  |  PCA-v1, active : False, n_channels : 102>, <Projection  |  PCA-v2, active : False, n_channels : 102>, <Projection  |  PCA-v3, active : False, n_channels : 102>, <Projection  |  Average EEG reference, active : False, n_channels : 60>, <Projection  |  ECG-planar--0.200-0.400-PCA-01, active : False, n_channels : 203>, <Projection  |  ECG-axial--0.200-0.400-PCA-01, active : False, n_channels : 102>]

Now let’s do EOG. Here we compute an EEG projector, and need to pass the measurement info so the topomap coordinates can be created.

projs, events = compute_proj_eog(raw, n_grad=1, n_mag=1, n_eeg=1, average=True)
print(projs)

eog_projs = projs[-3:]
mne.viz.plot_projs_topomap(eog_projs, info=raw.info)
../_images/sphx_glr_plot_artifacts_correction_ssp_002.png

Out:

[<Projection  |  PCA-v1, active : False, n_channels : 102>, <Projection  |  PCA-v2, active : False, n_channels : 102>, <Projection  |  PCA-v3, active : False, n_channels : 102>, <Projection  |  Average EEG reference, active : False, n_channels : 60>, <Projection  |  EOG-planar--0.200-0.200-PCA-01, active : False, n_channels : 203>, <Projection  |  EOG-axial--0.200-0.200-PCA-01, active : False, n_channels : 102>, <Projection  |  EOG-eeg--0.200-0.200-PCA-01, active : False, n_channels : 59>]

Apply SSP projections

MNE is handling projections at the level of the info, so to register them populate the list that you find in the ‘proj’ field

raw.info['projs'] += eog_projs + ecg_projs

Yes this was it. Now MNE will apply the projs on demand at any later stage, so watch out for proj parmeters in functions or to it explicitly with the .apply_proj method

Demonstrate SSP cleaning on some evoked data

events = mne.find_events(raw, stim_channel='STI 014')
reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)
# this can be highly data dependent
event_id = {'auditory/left': 1}

epochs_no_proj = mne.Epochs(raw, events, event_id, tmin=-0.2, tmax=0.5,
                            proj=False, baseline=(None, 0), reject=reject)
epochs_no_proj.average().plot(spatial_colors=True)


epochs_proj = mne.Epochs(raw, events, event_id, tmin=-0.2, tmax=0.5, proj=True,
                         baseline=(None, 0), reject=reject)
epochs_proj.average().plot(spatial_colors=True)
  • ../_images/sphx_glr_plot_artifacts_correction_ssp_003.png
  • ../_images/sphx_glr_plot_artifacts_correction_ssp_004.png

Looks cool right? It is however often not clear how many components you should take and unfortunately this can have bad consequences as can be seen interactively using the delayed SSP mode:

evoked = mne.Epochs(raw, events, event_id, tmin=-0.2, tmax=0.5,
                    proj='delayed', baseline=(None, 0),
                    reject=reject).average()

# set time instants in seconds (from 50 to 150ms in a step of 10ms)
times = np.arange(0.05, 0.15, 0.01)

fig = evoked.plot_topomap(times, proj='interactive')
  • ../_images/sphx_glr_plot_artifacts_correction_ssp_005.png
  • ../_images/sphx_glr_plot_artifacts_correction_ssp_006.png

now you should see checkboxes. Remove a few SSP and see how the auditory pattern suddenly drops off

Total running time of the script: ( 0 minutes 10.641 seconds)

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