Create topographic ERF maps in delayed SSP modeΒΆ

This script shows how to apply SSP projectors delayed, that is, at the evoked stage. This is particularly useful to support decisions related to the trade-off between denoising and preserving signal. In this example we demonstrate how to use topographic maps for delayed SSP application.

# Authors: Denis Engemann <>
#          Christian Brodbeck <>
#          Alexandre Gramfort <>
# License: BSD (3-clause)

import numpy as np
import mne
from mne import io
from mne.datasets import sample


data_path = sample.data_path()

Set parameters

raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
ecg_fname = data_path + '/MEG/sample/sample_audvis_ecg_proj.fif'
event_id, tmin, tmax = 1, -0.2, 0.5

# Setup for reading the raw data
raw = io.Raw(raw_fname)
events = mne.read_events(event_fname)

# delete EEG projections (we know it's the last one)
# add ECG projs for magnetometers
[raw.add_proj(p) for p in mne.read_proj(ecg_fname) if 'axial' in p['desc']]

# pick magnetometer channels
picks = mne.pick_types(, meg='mag', stim=False, eog=True,
                       include=[], exclude='bads')

# We will make of the proj `delayed` option to
# interactively select projections at the evoked stage.
# more information can be found in the example/
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), reject=dict(mag=4e-12), proj='delayed')

evoked = epochs.average()  # average epochs and get an Evoked dataset.

Interactively select / deselect the SSP projection vectors

# set time instants in seconds (from 50 to 150ms in a step of 10ms)
times = np.arange(0.05, 0.15, 0.01)

evoked.plot_topomap(times, proj='interactive')
# Hint: the same works for evoked.plot and evoked.plot_topo
  • ../../_images/sphx_glr_plot_evoked_topomap_delayed_ssp_001.png
  • ../../_images/sphx_glr_plot_evoked_topomap_delayed_ssp_002.png

Total running time of the script: (10 minutes 52.610 seconds)

Download Python source code: