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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.

# Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

importnumpyasnp
importmne
frommneimport find_events , fit_dipole
frommne.datasets.brainstormimport bst_phantom_elekta
frommne.ioimport 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).

dipole_number = 1
data_path = bst_phantom_elekta.data_path ()
raw = read_raw_fif (data_path / "kojak_all_200nAm_pp_no_chpi_no_ms_raw.fif")
raw.crop (40.0, 50.0).load_data()
order = list(range(160, 170))
raw.copy ().filter(0.0, 40.0).plot(order =order , n_channels=10)
Raw plot
Opening raw data file /home/circleci/mne_data/MNE-brainstorm-data/bst_phantom_elekta/kojak_all_200nAm_pp_no_chpi_no_ms_raw.fif...
 Read a total of 13 projection items:
 planar-0.0-115.0-PCA-01 (1 x 306) idle
 planar-0.0-115.0-PCA-02 (1 x 306) idle
 planar-0.0-115.0-PCA-03 (1 x 306) idle
 planar-0.0-115.0-PCA-04 (1 x 306) idle
 planar-0.0-115.0-PCA-05 (1 x 306) idle
 axial-0.0-115.0-PCA-01 (1 x 306) idle
 axial-0.0-115.0-PCA-02 (1 x 306) idle
 axial-0.0-115.0-PCA-03 (1 x 306) idle
 axial-0.0-115.0-PCA-04 (1 x 306) idle
 axial-0.0-115.0-PCA-05 (1 x 306) idle
 axial-0.0-115.0-PCA-06 (1 x 306) idle
 axial-0.0-115.0-PCA-07 (1 x 306) idle
 axial-0.0-115.0-PCA-08 (1 x 306) idle
 Range : 47000 ... 437999 = 47.000 ... 437.999 secs
Ready.
Reading 0 ... 10000 = 0.000 ... 10.000 secs...
Filtering raw data in 1 contiguous segment
Setting up low-pass filter at 40 Hz
FIR filter parameters
---------------------
Designing a one-pass, zero-phase, non-causal lowpass filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Upper passband edge: 40.00 Hz
- Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz)
- Filter length: 331 samples (0.331 s)

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

Raw plot
Processing MEG data using oversampled temporal projection
 Processing 1 data chunk of (at least) 10.0 s with 5.0 s overlap and hann windowing
 The final 0.001 s will be lumped into the final window
 Denoising 0.00 – 10.00 s
 Denoising 10.00 – 10.00 s
Filtering raw data in 1 contiguous segment
Setting up low-pass filter at 40 Hz
FIR filter parameters
---------------------
Designing a one-pass, zero-phase, non-causal lowpass filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Upper passband edge: 40.00 Hz
- Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz)
- Filter length: 331 samples (0.331 s)

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

defcompute_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, 0.0, 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(copy=False)[:, :, idx].T
 evoked = mne.EvokedArray (data, epochs.info, tmin=0.0)
 dip = fit_dipole (evoked, cov, sphere, 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(f"Raw bias: {np.mean (bias ):0.1f}mm (worst: {np.max (bias ):0.1f}mm)")
bias_clean = compute_bias(raw_clean )
print(f"OTP bias: {np.mean (bias_clean ):0.1f}mm (worst: {np.max (bias_clean ):0.1f}m)")

Raw bias: 2.5mm (worst: 5.1mm)
OTP bias: 1.2mm (worst: 1.3m)

References#

Total running time of the script: (0 minutes 24.101 seconds)

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