Python专栏 | ICA应用：如何识别伪影信号？（二）

系列文章目录

Python专栏 | 脑电图和脑磁图(EEG/MEG)的数据分析方法之载入数据

Python专栏 | MNE脑电数据(EEG/MEG)可视化

Python专栏 | MNE数据预处理方法——独立成分分析

Python专栏 | 独立成分分析(ICA)的实例应用：消除伪影信号

Python专栏 | ICA应用：如何识别伪影信号？（一）

持续更新中……

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Using a simulated channel to select ICA components

如果您没有测EOG信号，则还需要采取另外的方法。

find_bads_eog具有ch_name参数，可以将其用作EOG的代替。

可以使用单个波段（single channel）；

也可以从前额脑电传感器波段（frontal EEG channels）创建双极性参照（bipolar reference）并将其用作虚拟的EOG波段。但是这需要建立在一个前提下：即前额叶EEG波段仅包含EOG，而不包含前额皮质中的大脑活动变化，或者前额皮质中的大脑活动变化可以忽略不计。

相比于EOG，ECG比较简单。find_bads_ecg可以使用磁力计或梯度仪波段的交叉波段平均（cross-channel averaging）来构建虚拟ECG波段。所以，MEG波段通常不必传递特定的波段名称。

find_bads_ecg包含两个参数：“ctps”（交叉实验阶段统计cross-trial phase statistics）和“correlation”（数据与ECG波段之间的皮尔逊相关）。

代码示例：

#通过模拟波段来选择ICA components

ica.exclude = []
# find which ICs match the ECG pattern
ecg_indices, ecg_scores = ica.find_bads_ecg(raw, method='correlation',
                                            threshold='auto')
ica.exclude = ecg_indices

# barplot of ICA component "ECG match" scores
ica.plot_scores(ecg_scores)

# plot diagnostics
ica.plot_properties(raw, picks=ecg_indices)

# plot ICs applied to raw data, with ECG matches highlighted
ica.plot_sources(raw, show_scrollbars=False)

# plot ICs applied to the averaged ECG epochs, with ECG matches highlighted
ica.plot_sources(ecg_evoked)

输出结果：

Using multitaper spectrum estimation with 7 DPSS windows
Not setting metadata
Not setting metadata
138 matching events found
No baseline correction applied
0 projection items activated
0 bad epochs dropped
C:\ProgramData\Anaconda3\lib\site-packages\mkl_fft\_numpy_fft.py:331: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  output = mkl_fft.rfft_numpy(a, n=n, axis=axis)

Creating RawArray with float64 data, n_channels=16, n_times=166800
    Range : 25800 ... 192599 =     42.956 ...   320.670 secs
Ready.

ECG输出的这几张图，如果仔细观察，可看到图中有两个IC上都出现了心跳信号，而我们却只捕获了其中的一个（ICA001）。

实际上，如果我们仔细查看plot_sources输出的两张图，放大，则看起来ICA014具有与ICA001同相的弱周期性分量。这值得我们重新运行带有更多components的ICA，以更好地独立出第二个心跳信号（ICA014）。

代码示例：

# refit the ICA with 30 components this time
new_ica = ICA(n_components=30, random_state=97)#这一次将components设置成30
new_ica.fit(filt_raw)

# find which ICs match the ECG pattern
ecg_indices, ecg_scores = new_ica.find_bads_ecg(raw, method='correlation',
                                                threshold='auto')
new_ica.exclude = ecg_indices

# barplot of ICA component "ECG match" scores
new_ica.plot_scores(ecg_scores)

# plot diagnostics
new_ica.plot_properties(raw, picks=ecg_indices)

# plot ICs applied to raw data, with ECG matches highlighted
new_ica.plot_sources(raw, show_scrollbars=False)

# plot ICs applied to the averaged ECG epochs, with ECG matches highlighted
new_ica.plot_sources(ecg_evoked)

输出结果：

filtering ICA sources
Setting up band-pass filter from 8 - 16 Hz

FIR filter parameters
---------------------
Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:
- Windowed frequency-domain design (firwin2) method
- Hann window
- Lower passband edge: 8.00
- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 7.75 Hz)
- Upper passband edge: 16.00 Hz
- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 16.25 Hz)
- Filter length: 6007 samples (10.001 sec)

filtering target
Setting up band-pass filter from 8 - 16 Hz

FIR filter parameters
---------------------
Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:
- Windowed frequency-domain design (firwin2) method
- Hann window
- Lower passband edge: 8.00
- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 7.75 Hz)
- Upper passband edge: 16.00 Hz
- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 16.25 Hz)
- Filter length: 6007 samples (10.001 sec)

C:\ProgramData\Anaconda3\lib\site-packages\mkl_fft\_numpy_fft.py:331: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  output = mkl_fft.rfft_numpy(a, n=n, axis=axis)

 Using multitaper spectrum estimation with 7 DPSS windows
Not setting metadata
Not setting metadata
138 matching events found
No baseline correction applied
0 projection items activated
0 bad epochs dropped
C:\ProgramData\Anaconda3\lib\site-packages\mkl_fft\_numpy_fft.py:331: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  output = mkl_fft.rfft_numpy(a, n=n, axis=axis)
Not setting metadata
Not setting metadata
138 matching events found
No baseline correction applied
0 projection items activated
0 bad epochs dropped

Creating RawArray with float64 data, n_channels=31, n_times=166800
    Range : 25800 ... 192599 =     42.956 ...   320.670 secs
Ready.

代码：

#clean up memory before moving on
del raw, filt_raw, ica, new_ica

总结

今天用到的代码总结：

#通过模拟波段来选择ICA components
ica.exclude = []
# find which ICs match the ECG pattern
ecg_indices, ecg_scores = ica.find_bads_ecg(raw, method='correlation',
                                            threshold='auto')
ica.exclude = ecg_indices


# barplot of ICA component "ECG match" scores
ica.plot_scores(ecg_scores)


# plot diagnostics
ica.plot_properties(raw, picks=ecg_indices)


# plot ICs applied to raw data, with ECG matches highlighted
ica.plot_sources(raw, show_scrollbars=False)


# plot ICs applied to the averaged ECG epochs, with ECG matches highlighted
ica.plot_sources(ecg_evoked)


#通过观察ECG生成的这几张图，发现ICA014也有可能是心跳伪影。故尝试增加components，将ICA014分离出来
# refit the ICA with 30 components this time
new_ica = ICA(n_components=30, random_state=97)#这一次将components设置成30
new_ica.fit(filt_raw)


# find which ICs match the ECG pattern
ecg_indices, ecg_scores = new_ica.find_bads_ecg(raw, method='correlation',
                                                threshold='auto')
new_ica.exclude = ecg_indices


# barplot of ICA component "ECG match" scores
new_ica.plot_scores(ecg_scores)


# plot diagnostics
new_ica.plot_properties(raw, picks=ecg_indices)


# plot ICs applied to raw data, with ECG matches highlighted
new_ica.plot_sources(raw, show_scrollbars=False)


# plot ICs applied to the averaged ECG epochs, with ECG matches highlighted
new_ica.plot_sources(ecg_evoked)


#clean up memory before moving on
del raw, filt_raw, ica, new_ica