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%matplotlib inline

Plot sensor denoising using oversampled temporal projection

This demonstrates denoising using the OTP algorithm [1]_ on data with with sensor artifacts (flux jumps) and random noise.



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# Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)

import os.path as op
import mne
import numpy as np

from mne import find_events, fit_dipole
from mne.datasets.brainstorm import bst_phantom_elekta
from mne.io import read_raw_fif

print(__doc__)

Plot the phantom data, lowpassed to get rid of high-frequency artifacts. We also crop to a single 10-second segment for speed. Notice that there are two large flux jumps on channel 1522 that could spread to other channels when performing subsequent spatial operations (e.g., Maxwell filtering, SSP, or ICA).



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dipole_number = 1
data_path = bst_phantom_elekta.data_path()
raw = read_raw_fif(
    op.join(data_path, 'kojak_all_200nAm_pp_no_chpi_no_ms_raw.fif'))
raw.crop(40., 50.).load_data()
order = list(range(160, 170))
raw.copy().filter(0., 40.).plot(order=order, n_channels=10)

Now we can clean the data with OTP, lowpass, and plot. The flux jumps have been suppressed alongside the random sensor noise.



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raw_clean = mne.preprocessing.oversampled_temporal_projection(raw)
raw_clean.filter(0., 40.)
raw_clean.plot(order=order, n_channels=10)

We can also look at the effect on single-trial phantom localization. See the tut-brainstorm-elekta-phantom for more information. Here we use a version that does single-trial localization across the 17 trials are in our 10-second window:



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def compute_bias(raw):
    events = find_events(raw, 'STI201', verbose=False)
    events = events[1:]  # first one has an artifact
    tmin, tmax = -0.2, 0.1
    epochs = mne.Epochs(raw, events, dipole_number, tmin, tmax,
                        baseline=(None, -0.01), preload=True, verbose=False)
    sphere = mne.make_sphere_model(r0=(0., 0., 0.), head_radius=None,
                                   verbose=False)
    cov = mne.compute_covariance(epochs, tmax=0, method='oas',
                                 rank=None, verbose=False)
    idx = epochs.time_as_index(0.036)[0]
    data = epochs.get_data()[:, :, idx].T
    evoked = mne.EvokedArray(data, epochs.info, tmin=0.)
    dip = fit_dipole(evoked, cov, sphere, n_jobs=1, verbose=False)[0]
    actual_pos = mne.dipole.get_phantom_dipoles()[0][dipole_number - 1]
    misses = 1000 * np.linalg.norm(dip.pos - actual_pos, axis=-1)
    return misses


bias = compute_bias(raw)
print('Raw bias: %0.1fmm (worst: %0.1fmm)'
      % (np.mean(bias), np.max(bias)))
bias_clean = compute_bias(raw_clean)
print('OTP bias: %0.1fmm (worst: %0.1fmm)'
      % (np.mean(bias_clean), np.max(bias_clean),))

References

.. [1] Larson E, Taulu S (2017). Reducing Sensor Noise in MEG and EEG Recordings Using Oversampled Temporal Projection. IEEE Transactions on Biomedical Engineering.