""" .. _tut-reject-data-spans: Rejecting bad data spans ======================== This tutorial covers manual marking of bad spans of data, and automated rejection of data spans based on signal amplitude. .. contents:: Page contents :local: :depth: 2 We begin as always by importing the necessary Python modules and loading some :ref:`example data `; to save memory we'll use a pre-filtered and downsampled version of the example data, and we'll also load an events array to use when converting the continuous data to epochs: """ import os import mne sample_data_folder = mne.datasets.sample.data_path() sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample', 'sample_audvis_filt-0-40_raw.fif') raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False) events_file = os.path.join(sample_data_folder, 'MEG', 'sample', 'sample_audvis_filt-0-40_raw-eve.fif') events = mne.read_events(events_file) ############################################################################### # Annotating bad spans of data # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # # The tutorial :ref:`tut-events-vs-annotations` describes how # :class:`~mne.Annotations` can be read from embedded events in the raw # recording file, and :ref:`tut-annotate-raw` describes in detail how to # interactively annotate a :class:`~mne.io.Raw` data object. Here, we focus on # best practices for annotating *bad* data spans so that they will be excluded # from your analysis pipeline. # # # The ``reject_by_annotation`` parameter # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # In the interactive ``raw.plot()`` window, the annotation controls can be # opened by pressing :kbd:`a`. Here, new annotation labels can be created or # existing annotation labels can be selected for use. fig = raw.plot() fig.canvas.key_press_event('a') ############################################################################### # .. sidebar:: Annotating good spans # # The default "BAD\_" prefix for new labels can be removed simply by # pressing the backspace key four times before typing your custom # annotation label. # # You can see that default annotation label is "BAD\_"; this can be edited # prior to pressing the "Add label" button to customize the label. The intent # is that users can annotate with as many or as few labels as makes sense for # their research needs, but that annotations marking spans that should be # excluded from the analysis pipeline should all begin with "BAD" or "bad" # (e.g., "bad_cough", "bad-eyes-closed", "bad door slamming", etc). When this # practice is followed, many processing steps in MNE-Python will automatically # exclude the "bad"-labelled spans of data; this behavior is controlled by a # parameter ``reject_by_annotation`` that can be found in many MNE-Python # functions or class constructors, including: # # - creation of epoched data from continuous data (:class:`mne.Epochs`) # - independent components analysis (:class:`mne.preprocessing.ICA`) # - functions for finding heartbeat and blink artifacts # (:func:`~mne.preprocessing.find_ecg_events`, # :func:`~mne.preprocessing.find_eog_events`) # - covariance computations (:func:`mne.compute_raw_covariance`) # - power spectral density computation (:meth:`mne.io.Raw.plot_psd`, # :func:`mne.time_frequency.psd_welch`) # # For example, when creating epochs from continuous data, if # ``reject_by_annotation=True`` the :class:`~mne.Epochs` constructor will drop # any epoch that partially or fully overlaps with an annotated span that begins # with "bad". # # # Generating annotations programmatically # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # # The :ref:`tut-artifact-overview` tutorial introduced the artifact detection # functions :func:`~mne.preprocessing.find_eog_events` and # :func:`~mne.preprocessing.find_ecg_events` (although that tutorial mostly # relied on their higher-level wrappers # :func:`~mne.preprocessing.create_eog_epochs` and # :func:`~mne.preprocessing.create_ecg_epochs`). Here, for demonstration # purposes, we make use of the lower-level artifact detection function to get # an events array telling us where the blinks are, then automatically add # "bad_blink" annotations around them (this is not necessary when using # :func:`~mne.preprocessing.create_eog_epochs`, it is done here just to show # how annotations are added non-interactively). We'll start the annotations # 250 ms before the blink and end them 250 ms after it: # sphinx_gallery_thumbnail_number = 3 eog_events = mne.preprocessing.find_eog_events(raw) onsets = eog_events[:, 0] / raw.info['sfreq'] - 0.25 durations = [0.5] * len(eog_events) descriptions = ['bad blink'] * len(eog_events) blink_annot = mne.Annotations(onsets, durations, descriptions, orig_time=raw.info['meas_date']) raw.set_annotations(blink_annot) ############################################################################### # Now we can confirm that the annotations are centered on the EOG events. Since # blinks are usually easiest to see in the EEG channels, we'll only plot EEG # here: eeg_picks = mne.pick_types(raw.info, meg=False, eeg=True) raw.plot(events=eog_events, order=eeg_picks) ############################################################################### # See the section :ref:`tut-section-programmatic-annotations` for more details # on creating annotations programmatically. # # # Rejecting Epochs based on channel amplitude # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # # Besides "bad" annotations, the :class:`mne.Epochs` class constructor has # another means of rejecting epochs, based on signal amplitude thresholds for # each channel type. In the :ref:`overview tutorial # ` we saw an example of this: setting maximum # acceptable peak-to-peak amplitudes for each channel type in an epoch, using # the ``reject`` parameter. There is also a related parameter, ``flat``, that # can be used to set *minimum* acceptable peak-to-peak amplitudes for each # channel type in an epoch: reject_criteria = dict(mag=3000e-15, # 3000 fT grad=3000e-13, # 3000 fT/cm eeg=100e-6, # 100 μV eog=200e-6) # 200 μV flat_criteria = dict(mag=1e-15, # 1 fT grad=1e-13, # 1 fT/cm eeg=1e-6) # 1 μV ############################################################################### # The values that are appropriate are dataset- and hardware-dependent, so some # trial-and-error may be necessary to find the correct balance between data # quality and loss of power due to too many dropped epochs. Here, we've set the # rejection criteria to be fairly stringent, for illustration purposes. # # Two additional parameters, ``reject_tmin`` and ``reject_tmax``, are used to # set the temporal window in which to calculate peak-to-peak amplitude for the # purposes of epoch rejection. These default to the same ``tmin`` and ``tmax`` # of the entire epoch. As one example, if you wanted to only apply the # rejection thresholds to the portion of the epoch that occurs *before* the # event marker around which the epoch is created, you could set # ``reject_tmax=0``. A summary of the causes of rejected epochs can be # generated with the :meth:`~mne.Epochs.plot_drop_log` method: epochs = mne.Epochs(raw, events, tmin=-0.2, tmax=0.5, reject_tmax=0, reject=reject_criteria, flat=flat_criteria, reject_by_annotation=False, preload=True) epochs.plot_drop_log() ############################################################################### # Notice that we've passed ``reject_by_annotation=False`` above, in order to # isolate the effects of the rejection thresholds. If we re-run the epoching # with ``reject_by_annotation=True`` (the default) we see that the rejections # due to EEG and EOG channels have disappeared (suggesting that those channel # fluctuations were probably blink-related, and were subsumed by rejections # based on the "bad blink" label). epochs = mne.Epochs(raw, events, tmin=-0.2, tmax=0.5, reject_tmax=0, reject=reject_criteria, flat=flat_criteria, preload=True) epochs.plot_drop_log() ############################################################################### # More importantly, note that *many* more epochs are rejected (~20% instead of # ~2.5%) when rejecting based on the blink labels, underscoring why it is # usually desirable to repair artifacts rather than exclude them. # # The :meth:`~mne.Epochs.plot_drop_log` method is a visualization of an # :class:`~mne.Epochs` attribute, namely ``epochs.drop_log``, which stores # empty lists for retained epochs and lists of strings for dropped epochs, with # the strings indicating the reason(s) why the epoch was dropped. For example: print(epochs.drop_log) ############################################################################### # Finally, it should be noted that "dropped" epochs are not necessarily deleted # from the :class:`~mne.Epochs` object right away. Above, we forced the # dropping to happen when we created the :class:`~mne.Epochs` object by using # the ``preload=True`` parameter. If we had not done that, the # :class:`~mne.Epochs` object would have been `memory-mapped`_ (not loaded into # RAM), in which case the criteria for dropping epochs are stored, and the # actual dropping happens when the :class:`~mne.Epochs` data are finally loaded # and used. There are several ways this can get triggered, such as: # # - explicitly loading the data into RAM with the :meth:`~mne.Epochs.load_data` # method # - plotting the data (:meth:`~mne.Epochs.plot`, # :meth:`~mne.Epochs.plot_image`, etc) # - using the :meth:`~mne.Epochs.average` method to create an # :class:`~mne.Evoked` object # # You can also trigger dropping with the :meth:`~mne.Epochs.drop_bad` method; # if ``reject`` and/or ``flat`` criteria have already been provided to the # epochs constructor, :meth:`~mne.Epochs.drop_bad` can be used without # arguments to simply delete the epochs already marked for removal (if the # epochs have already been dropped, nothing further will happen): epochs.drop_bad() ############################################################################### # Alternatively, if rejection thresholds were not originally given to the # :class:`~mne.Epochs` constructor, they can be passed to # :meth:`~mne.Epochs.drop_bad` later instead; this can also be a way of # imposing progressively more stringent rejection criteria: stronger_reject_criteria = dict(mag=2000e-15, # 2000 fT grad=2000e-13, # 2000 fT/cm eeg=100e-6, # 100 μV eog=100e-6) # 100 μV epochs.drop_bad(reject=stronger_reject_criteria) print(epochs.drop_log) ############################################################################### # Note that a complementary Python module, the `autoreject package`_, uses # machine learning to find optimal rejection criteria, and is designed to # integrate smoothly with MNE-Python workflows. This can be a considerable # time-saver when working with heterogeneous datasets. # # # .. LINKS # # .. _`memory-mapped`: https://en.wikipedia.org/wiki/Memory-mapped_file # .. _`autoreject package`: http://autoreject.github.io/