XDAWN Denoising

XDAWN filters are trained from epochs, signal is projected in the sources space and then projected back in the sensor space using only the first two XDAWN components. The process is similar to an ICA, but is supervised in order to maximize the signal to signal + noise ratio of the evoked response 12.

Warning

As this denoising method exploits the known events to maximize SNR of the contrast between conditions it can lead to overfitting. To avoid a statistical analysis problem you should split epochs used in fit with the ones used in apply method.

# Authors: Alexandre Barachant <alexandre.barachant@gmail.com>
#
# License: BSD (3-clause)


from mne import (io, compute_raw_covariance, read_events, pick_types, Epochs)
from mne.datasets import sample
from mne.preprocessing import Xdawn
from mne.viz import plot_epochs_image

print(__doc__)

data_path = sample.data_path()

Set parameters and read data

raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
tmin, tmax = -0.1, 0.3
event_id = dict(vis_r=4)

# Setup for reading the raw data
raw = io.read_raw_fif(raw_fname, preload=True)
raw.filter(1, 20, fir_design='firwin')  # replace baselining with high-pass
events = read_events(event_fname)

raw.info['bads'] = ['MEG 2443']  # set bad channels
picks = pick_types(raw.info, meg=True, eeg=False, stim=False, eog=False,
                   exclude='bads')
# Epoching
epochs = Epochs(raw, events, event_id, tmin, tmax, proj=False,
                picks=picks, baseline=None, preload=True,
                verbose=False)

# Plot image epoch before xdawn
plot_epochs_image(epochs['vis_r'], picks=[230], vmin=-500, vmax=500)

Out:

Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
    Read a total of 4 projection items:
        PCA-v1 (1 x 102)  idle
        PCA-v2 (1 x 102)  idle
        PCA-v3 (1 x 102)  idle
        Average EEG reference (1 x 60)  idle
    Range : 6450 ... 48149 =     42.956 ...   320.665 secs
Ready.
Reading 0 ... 41699  =      0.000 ...   277.709 secs...
Filtering raw data in 1 contiguous segment
Setting up band-pass filter from 1 - 20 Hz

FIR filter parameters
---------------------
Designing a one-pass, zero-phase, non-causal bandpass filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Lower passband edge: 1.00
- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)
- Upper passband edge: 20.00 Hz
- Upper transition bandwidth: 5.00 Hz (-6 dB cutoff frequency: 22.50 Hz)
- Filter length: 497 samples (3.310 sec)

Not setting metadata
Not setting metadata
70 matching events found
No baseline correction applied
0 projection items activated
0 bad epochs dropped

Now, we estimate a set of xDAWN filters for the epochs (which contain only the vis_r class).

# Estimates signal covariance
signal_cov = compute_raw_covariance(raw, picks=picks)

# Xdawn instance
xd = Xdawn(n_components=2, signal_cov=signal_cov)

# Fit xdawn
xd.fit(epochs)

Out:

Using up to 1388 segments
Number of samples used : 41640
[done]
Computing rank from data with rank='full'
    MEG: rank 305 from info
    Created an SSP operator (subspace dimension = 3)
Reducing data rank from 305 -> 305
Estimating covariance using EMPIRICAL
Done.

Epochs are denoised by calling apply, which by default keeps only the signal subspace corresponding to the first n_components specified in the Xdawn constructor above.

epochs_denoised = xd.apply(epochs)

# Plot image epoch after Xdawn
plot_epochs_image(epochs_denoised['vis_r'], picks=[230], vmin=-500, vmax=500)

Out:

Transforming to Xdawn space
Zeroing out 303 Xdawn components
Inverse transforming to sensor space
Not setting metadata
Not setting metadata
70 matching events found
No baseline correction applied
0 projection items activated
0 bad epochs dropped

References

1

Bertrand Rivet, Antoine Souloumiac, Virginie Attina, and Guillaume Gibert. xDAWN algorithm to enhance evoked potentials: application to brain–computer interface. IEEE Transactions on Biomedical Engineering, 56(8):2035–2043, 2009. doi:10.1109/TBME.2009.2012869.

2

Bertrand Rivet, Hubert Cecotti, Antoine Souloumiac, Emmanuel Maby, and Jérémie Mattout. Theoretical analysis of xDAWN algorithm: application to an efficient sensor selection in a P300 BCI. In Proceedings of EUSIPCO-2011, 1382–1386. Barcelona, 2011. IEEE. URL: https://ieeexplore.ieee.org/document/7073970.

Estimated memory usage: 128 MB

Remap MEG channel types How to convert 3D electrode positions to a 2D image.