非官方中文翻译 本页译自 MNE-Python 官方文档： 查看英文原文 ↗。 文档版权归 MNE Developers 所有（BSD-3-Clause）。

注意

转到末尾下载完整示例代码。

绘制白化数据#

本教程演示如何绘制白化后的诱发数据。

许多处理过程都会对数据进行白化，包括偶极子拟合、源定位以及部分解码算法。因此，查看白化数据可以从这些算法所处理的数据角度提供一个不同的视角。

我们先加载一些数据，并计算一个我们将视为噪声的信号（空间）协方差。

# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

import mne
from mne.datasets import sample

白化后的原始数据#

注意

在传入 noise_cov 的 mne.io.Raw.plot() 中，你可以按 w 键开启或关闭白化。

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)

events = mne.find_events(raw, stim_channel="STI 014")
event_id = {
    "auditory/left": 1,
    "auditory/right": 2,
    "visual/left": 3,
    "visual/right": 4,
    "smiley": 5,
    "button": 32,
}
reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)
epochs = mne.Epochs(raw, events, event_id=event_id, reject=reject)

# baseline noise cov, not a lot of samples
noise_cov = mne.compute_covariance(
    epochs, tmax=0.0, method="shrunk", rank=None, verbose="error"
)

# butterfly mode shows the differences most clearly
raw.plot(events=events, butterfly=True)
raw.plot(noise_cov=noise_cov, events=events, butterfly=True)
  • Raw plots
  • Raw plots
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...
Finding events on: STI 014
319 events found on stim channel STI 014
Event IDs: [ 1  2  3  4  5 32]
Not setting metadata
319 matching events found
Setting baseline interval to [-0.19979521315838786, 0.0] s
Applying baseline correction (mode: mean)
Created an SSP operator (subspace dimension = 4)
4 projection items activated
Computing rank from covariance with rank=None
    Using tolerance 8.3e-14 (2.2e-16 eps * 59 dim * 6.3  max singular value)
    Estimated rank (eeg): 58
    EEG: rank 58 computed from 59 data channels with 1 projector
Computing rank from covariance with rank=None
    Using tolerance 1.9e-13 (2.2e-16 eps * 203 dim * 4.3  max singular value)
    Estimated rank (grad): 203
    GRAD: rank 203 computed from 203 data channels with 0 projectors
Computing rank from covariance with rank=None
    Using tolerance 2.7e-14 (2.2e-16 eps * 102 dim * 1.2  max singular value)
    Estimated rank (mag): 99
    MAG: rank 99 computed from 102 data channels with 3 projectors

白化后的 Epochs 数据#

epochs.plot(events=True)
epochs.plot(noise_cov=noise_cov, events=True)
  • Raw plots
  • Raw plots
Using data from preloaded Raw for 319 events and 106 original time points ...
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on MAG : ['MEG 1711']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on MAG : ['MEG 1711']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
    Rejecting  epoch based on EOG : ['EOG 061']
51 bad epochs dropped
You seem to have overlapping epochs. Some event lines may be duplicated in the plot.
Using data from preloaded Raw for 20 events and 106 original time points ...
Using data from preloaded Raw for 20 events and 106 original time points ...
Using data from preloaded Raw for 20 events and 106 original time points ...
You seem to have overlapping epochs. Some event lines may be duplicated in the plot.
Computing rank from covariance with rank=None
    Using tolerance 8.3e-14 (2.2e-16 eps * 59 dim * 6.3  max singular value)
    Estimated rank (eeg): 58
    EEG: rank 58 computed from 59 data channels with 1 projector
Computing rank from covariance with rank=None
    Using tolerance 1.9e-13 (2.2e-16 eps * 203 dim * 4.3  max singular value)
    Estimated rank (grad): 203
    GRAD: rank 203 computed from 203 data channels with 0 projectors
Computing rank from covariance with rank=None
    Using tolerance 2.7e-14 (2.2e-16 eps * 102 dim * 1.2  max singular value)
    Estimated rank (mag): 99
    MAG: rank 99 computed from 102 data channels with 3 projectors
Using data from preloaded Raw for 20 events and 106 original time points ...
Using data from preloaded Raw for 20 events and 106 original time points ...
Using data from preloaded Raw for 20 events and 106 original time points ...

白化后的诱发数据#

evoked = epochs.average()
evoked.plot(time_unit="s")
evoked.plot(noise_cov=noise_cov, time_unit="s")
  • EEG (59 channels), Gradiometers (203 channels), Magnetometers (102 channels)
  • EEG (59 channels), Gradiometers (203 channels), Magnetometers (102 channels)
Computing rank from covariance with rank=None
    Using tolerance 8.3e-14 (2.2e-16 eps * 59 dim * 6.3  max singular value)
    Estimated rank (eeg): 58
    EEG: rank 58 computed from 59 data channels with 1 projector
Computing rank from covariance with rank=None
    Using tolerance 1.9e-13 (2.2e-16 eps * 203 dim * 4.3  max singular value)
    Estimated rank (grad): 203
    GRAD: rank 203 computed from 203 data channels with 0 projectors
Computing rank from covariance with rank=None
    Using tolerance 2.7e-14 (2.2e-16 eps * 102 dim * 1.2  max singular value)
    Estimated rank (mag): 99
    MAG: rank 99 computed from 102 data channels with 3 projectors

缩放白化后的诱发数据#

函数 mne.Evoked.plot_white() 还会进一步缩放白化图，以显示数据对高斯噪声假设的满足程度：

evoked.plot_white(noise_cov=noise_cov, time_unit="s")
EEG (59 channels), Gradiometers (203 channels), Magnetometers (102 channels), Whitened GFP, method = 
NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
Computing rank from covariance with rank=None
    Using tolerance 8.3e-14 (2.2e-16 eps * 59 dim * 6.3  max singular value)
    Estimated rank (eeg): 58
    EEG: rank 58 computed from 59 data channels with 1 projector
Computing rank from covariance with rank=None
    Using tolerance 1.9e-13 (2.2e-16 eps * 203 dim * 4.3  max singular value)
    Estimated rank (grad): 203
    GRAD: rank 203 computed from 203 data channels with 0 projectors
Computing rank from covariance with rank=None
    Using tolerance 2.7e-14 (2.2e-16 eps * 102 dim * 1.2  max singular value)
    Estimated rank (mag): 99
    MAG: rank 99 computed from 102 data channels with 3 projectors
    Created an SSP operator (subspace dimension = 4)
Computing rank from covariance with rank={'eeg': 58, 'grad': 203, 'mag': 99, 'meg': 302}
    Setting small MEG eigenvalues to zero (without PCA)
    Setting small EEG eigenvalues to zero (without PCA)
    Created the whitener using a noise covariance matrix with rank 360 (4 small eigenvalues omitted)

白化数据的地形图#

evoked.comment = "All trials"
evoked.plot_topo(title="Evoked data")
evoked.plot_topo(noise_cov=noise_cov, title="Whitened evoked data")
  • 40 whitened
  • 40 whitened
    Created an SSP operator (subspace dimension = 4)
Computing rank from covariance with rank=None
    Using tolerance 3.6e-13 (2.2e-16 eps * 305 dim * 5.3  max singular value)
    Estimated rank (mag + grad): 302
    MEG: rank 302 computed from 305 data channels with 3 projectors
    Using tolerance 8.3e-14 (2.2e-16 eps * 59 dim * 6.3  max singular value)
    Estimated rank (eeg): 58
    EEG: rank 58 computed from 59 data channels with 1 projector
    Setting small MEG eigenvalues to zero (without PCA)
    Setting small EEG eigenvalues to zero (without PCA)
    Created the whitener using a noise covariance matrix with rank 360 (4 small eigenvalues omitted)

脚本总运行时间：（0 分钟 10.624 秒）

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