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XDAWN 去噪#
XDAWN 滤波器由分段训练得到，把信号投影到源空间，再只用前两个 XDAWN 成分投影回传感器空间。该过程类似 ICA，但为监督式，以最大化诱发响应的信号/（信号+噪声）比 [1] [2]。
警告
由于该去噪方法利用已知事件来最大化条件间对比的信噪比，可能导致过拟合。为避免统计分析问题，应把 fit 使用的分段与 apply 使用的分段分开。
# Authors: Alexandre Barachant <alexandre.barachant@gmail.com>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from mne import Epochs, compute_raw_covariance, io, pick_types, read_events
from mne.datasets import sample
from mne.preprocessing import Xdawn
from mne.viz import plot_epochs_image
print(__doc__)
data_path = sample.data_path()
设置参数并读取数据
meg_path = data_path / "MEG" / "sample"
raw_fname = meg_path / "sample_audvis_filt-0-40_raw.fif"
event_fname = meg_path / "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, applying the SSP projectors that come with this dataset
raw.apply_proj()
epochs = Epochs(
raw,
events,
event_id,
tmin,
tmax,
proj=True,
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)
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 s)
Created an SSP operator (subspace dimension = 4)
4 projection items activated
SSP projectors applied...
Not setting metadata
70 matching events found
No baseline correction applied
0 projection items activated
现在为分段（只含 vis_r 类）估计一组 xDAWN 滤波器。
应用上述三个 SSP 投影会使数据秩亏（302 而非 305），因此 xDAWN 依赖的广义特征值分解条件很差（甚至可能直接失败），除非告知它数据的秩。传入 rank="info" 可把分解限制在信号协方差的 302 维主子空间，并把所得滤波器与模式投影回 305 个传感器。
# Estimates signal covariance
signal_cov = compute_raw_covariance(raw, picks=picks)
# Xdawn instance
xd = Xdawn(n_components=2, signal_cov=signal_cov, rank="info")
# Fit xdawn
xd.fit(epochs)
Using up to 1388 segments
Number of samples used : 41640
[done]
Computing rank from data with rank='info'
data: rank 302 after 3 projectors applied to 305 channels
Created an SSP operator (subspace dimension = 3)
Setting small data eigenvalues to zero (without PCA)
Estimating covariance using EMPIRICAL
Done.
调用 apply 对分段去噪，默认只保留上面 Xdawn 构造函数中 n_components 指定的前若干个成分所对应的信号子空间。
epochs_denoised = xd.apply(epochs)
# Plot image epoch after Xdawn
plot_epochs_image(epochs_denoised["vis_r"], picks=[230], vmin=-500, vmax=500)
Transforming to Xdawn space
Zeroing out 300 Xdawn components
Inverse transforming to sensor space
Not setting metadata
70 matching events found
No baseline correction applied
0 projection items activated
参考文献#
脚本总运行时间：（0 分 2.444 秒）