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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Compute ICA components on epochs
ICA is fit to MEG raw data.
We assume that the non-stationary EOG artifacts have already been removed.
The sources matching the ECG are automatically found and displayed.
Note that this example does quite a bit of processing, so even on a
fast machine it can take about a minute to complete.
Step1:  Read and preprocess the data. Preprocessing consists of
Step2:  Fit ICA model using the FastICA algorithm, detect and plot components
explaining ECG artifacts.
Step3:  Plot properties of ECG components
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Python Code:
# Authors: Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.preprocessing import ICA, create_ecg_epochs
from mne.datasets import sample
print(__doc__)
Explanation: Compute ICA components on epochs
ICA is fit to MEG raw data.
We assume that the non-stationary EOG artifacts have already been removed.
The sources matching the ECG are automatically found and displayed.
Note that this example does quite a bit of processing, so even on a
fast machine it can take about a minute to complete.
End of explanation
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)
raw.pick_types(meg=True, eeg=False, exclude='bads', stim=True)
raw.filter(1, 30)
# longer + more epochs for more artifact exposure
events = mne.find_events(raw, stim_channel='STI 014')
epochs = mne.Epochs(raw, events, event_id=None, tmin=-0.2, tmax=0.5)
Explanation: Read and preprocess the data. Preprocessing consists of:
meg channel selection
1 - 30 Hz band-pass IIR filter
epoching -0.2 to 0.5 seconds with respect to events
End of explanation
ica = ICA(n_components=0.95, method='fastica').fit(epochs)
ecg_epochs = create_ecg_epochs(raw, tmin=-.5, tmax=.5)
ecg_inds, scores = ica.find_bads_ecg(ecg_epochs)
ica.plot_components(ecg_inds)
Explanation: Fit ICA model using the FastICA algorithm, detect and plot components
explaining ECG artifacts.
End of explanation
ica.plot_properties(epochs, picks=ecg_inds)
Explanation: Plot properties of ECG components:
End of explanation
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