old_contents: # doc:slow-example
  """
  ================================
  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.
  Subsequently, artefact detection and rejection quality are assessed.
  Finally, the impact on the evoked ERF is visualized.
  
  Note that this example does quite a bit of processing, so even on a
  fast machine it can take about a minute to complete.
  """
  
  # Authors: Denis Engemann <denis.engemann@gmail.com>
  #
  # License: BSD (3-clause)
  
  import numpy as np
  
  import mne
  from mne.io import Raw
  from mne.preprocessing import ICA, create_ecg_epochs
  from mne.datasets import sample
  
  print(__doc__)
  
  ###############################################################################
  # Setup paths and prepare epochs data
  
  data_path = sample.data_path()
  raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
  
  raw = Raw(raw_fname, preload=True)
  raw.filter(1, 30, method='iir')
  raw.pick_types(meg=True, eeg=False, exclude='bads', stim=True)
  
  # 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)
  
  ###############################################################################
  # Fit ICA model using the FastICA algorithm, detect and inspect components
  
  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_sources(epochs, exclude=ecg_inds, title='Sources related to %s '
                   'artifacts (red)' % 'ecg')
  
new_contents: # doc:slow-example
  """
  ================================
  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.
  Subsequently, artefact detection and rejection quality are assessed.
  Finally, the impact on the evoked ERF is visualized.
  
  Note that this example does quite a bit of processing, so even on a
  fast machine it can take about a minute to complete.
  """
  
  # Authors: Denis Engemann <denis.engemann@gmail.com>
  #
  # License: BSD (3-clause)
  
  import numpy as np
  
  import mne
  from mne.io import Raw
  from mne.preprocessing import ICA, create_ecg_epochs
  from mne.datasets import sample
  
  print(__doc__)
  
  ###############################################################################
  # Fit ICA model using the FastICA algorithm, detect and inspect components
  
  data_path = sample.data_path()
  raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
  
  raw = Raw(raw_fname, preload=True)
  raw.filter(1, 30, method='iir')
  raw.pick_types(meg=True, eeg=False, exclude='bads', stim=True)
  
  # 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)
  
  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)
  
subject: convert to exmaple ica epochs