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）。

说明

转到末尾下载完整示例代码。

使用 SPoC 进行连续目标解码#

源功率共调制（SPoC）[1] 可以找出与连续目标最大相关的正交空间滤波器的组合。

SPoC 可视为 CSP 在连续变量上的扩展。

这里使用 FieldTrip 的皮层—肌肉相干性（Cortico-Muscular Coherence）示例数据，从 MEG beta 活动解码肌电图的（连续）波动。

# Author: Alexandre Barachant <alexandre.barachant@gmail.com>
#         Jean-Rémi King <jeanremi.king@gmail.com>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

import matplotlib.pyplot as plt
from sklearn.linear_model import Ridge
from sklearn.model_selection import KFold, cross_val_predict
from sklearn.pipeline import make_pipeline

import mne
from mne import Epochs
from mne.datasets.fieldtrip_cmc import data_path
from mne.decoding import SPoC, get_spatial_filter_from_estimator

# Define parameters
fname = data_path() / "SubjectCMC.ds"
raw = mne.io.read_raw_ctf(fname)
raw.crop(50.0, 200.0)  # crop for memory purposes

# Filter muscular activity to only keep high frequencies
emg = raw.copy().pick(["EMGlft"]).load_data()
emg.filter(20.0, None)

# Filter MEG data to focus on beta band
raw.pick(picks=["meg", "ref_meg"]).load_data()
raw.filter(15.0, 30.0)

# Build epochs as sliding windows over the continuous raw file
events = mne.make_fixed_length_events(raw, id=1, duration=0.75)

# Epoch length is 1.5 second
meg_epochs = Epochs(raw, events, tmin=0.0, tmax=1.5, baseline=None, detrend=1, decim=12)
emg_epochs = Epochs(emg, events, tmin=0.0, tmax=1.5, baseline=None)

# Prepare classification
X = meg_epochs.get_data()
y = emg_epochs.get_data().var(axis=2)[:, 0]  # target is EMG power

# Classification pipeline with SPoC spatial filtering and Ridge Regression
spoc = SPoC(n_components=2, log=True, reg="oas", rank="full")
clf = make_pipeline(spoc, Ridge(random_state=127))
# Define a two fold cross-validation
cv = KFold(n_splits=2, shuffle=False)

# Run cross validation
y_preds = cross_val_predict(clf, X, y, cv=cv)

# Plot the True EMG power and the EMG power predicted from MEG data
fig, ax = plt.subplots(1, 1, figsize=[10, 4], layout="constrained")
times = raw.times[meg_epochs.events[:, 0] - raw.first_samp]
ax.plot(times, y_preds, color="b", label="Predicted EMG")
ax.plot(times, y, color="r", label="True EMG")
ax.set_xlabel("Time (s)")
ax.set_ylabel("EMG Power")
ax.set_title("SPoC MEG Predictions")
plt.legend()
plt.show()
SPoC MEG Predictions
ds directory : /home/circleci/mne_data/MNE-fieldtrip_cmc-data/SubjectCMC.ds
    res4 data read.
    hc data read.
    Separate EEG position data file not present.
    Quaternion matching (desired vs. transformed):
       0.33   78.32    0.00 mm <->    0.33   78.32    0.00 mm (orig :  -71.62   40.46 -256.48 mm) diff =    0.000 mm
      -0.33  -78.32   -0.00 mm <->   -0.33  -78.32   -0.00 mm (orig :   39.27  -70.16 -258.60 mm) diff =    0.000 mm
     114.65    0.00   -0.00 mm <->  114.65    0.00   -0.00 mm (orig :   64.35   66.64 -262.01 mm) diff =    0.000 mm
    Coordinate transformations established.
    Polhemus data for 3 HPI coils added
    Device coordinate locations for 3 HPI coils added
Picked positions of 4 EEG channels from channel info
    4 EEG locations added to Polhemus data.
    Measurement info composed.
Finding samples for /home/circleci/mne_data/MNE-fieldtrip_cmc-data/SubjectCMC.ds/SubjectCMC.meg4:
    System clock channel is available, checking which samples are valid.
    75 x 12000 = 911610 samples from 191 chs
    390 samples omitted at the end
Current compensation grade : 0
Reading 0 ... 180000  =      0.000 ...   150.000 secs...
Filtering raw data in 1 contiguous segment
Setting up high-pass filter at 20 Hz

FIR filter parameters
---------------------
Designing a one-pass, zero-phase, non-causal highpass filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Lower passband edge: 20.00
- Lower transition bandwidth: 5.00 Hz (-6 dB cutoff frequency: 17.50 Hz)
- Filter length: 793 samples (0.661 s)

Reading 0 ... 180000  =      0.000 ...   150.000 secs...
Filtering raw data in 1 contiguous segment
Setting up band-pass filter from 15 - 30 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: 15.00
- Lower transition bandwidth: 3.75 Hz (-6 dB cutoff frequency: 13.12 Hz)
- Upper passband edge: 30.00 Hz
- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
- Filter length: 1057 samples (0.881 s)

Not setting metadata
200 matching events found
No baseline correction applied
0 projection items activated
Not setting metadata
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No baseline correction applied
0 projection items activated
Using data from preloaded Raw for 200 events and 1801 original time points (prior to decimation) ...
1 bad epochs dropped
Using data from preloaded Raw for 200 events and 1801 original time points ...
1 bad epochs dropped
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绘制各检测成分的贡献（即正向模型）

spoc.fit(X, y)
spf = get_spatial_filter_from_estimator(spoc, info=meg_epochs.info)
spf.plot_scree()

# Plot patterns for the first three components
# with largest absolute generalized eigenvalues,
# as we can see on the scree plot
spf.plot_patterns(components=[0, 1, 2])
  • Scree plot
  • Pattern0, Pattern1, Pattern2, AU
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参考文献#

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