""" Rejecting bad data (channels and segments) ========================================== """ # sphinx_gallery_thumbnail_number = 3 import numpy as np import mne from mne.datasets import sample 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) # already has an EEG ref ############################################################################### # .. _marking_bad_channels: # # Marking bad channels # -------------------- # # Sometimes some MEG or EEG channels are not functioning properly # for various reasons. These channels should be excluded from # analysis by marking them bad as. This is done by setting the 'bads' # in the measurement info of a data container object (e.g. Raw, Epochs, # Evoked). The info['bads'] value is a Python string. Here is # example: raw.info['bads'] = ['MEG 2443'] ############################################################################### # **Why setting a channel bad?**: If a channel does not show # a signal at all (flat) it is important to exclude it from the # analysis. If a channel as a noise level significantly higher than the # other channels it should be marked as bad. Presence of bad channels # can have terribe consequences on down stream analysis. For a flat channel # some noise estimate will be unrealistically low and # thus the current estimate calculations will give a strong weight # to the zero signal on the flat channels and will essentially vanish. # Noisy channels can also affect others when signal-space projections # or EEG average electrode reference is employed. Noisy bad channels can # also adversely affect averaging and noise-covariance matrix estimation by # causing unnecessary rejections of epochs. # # Recommended ways to identify bad channels are: # # - Observe the quality of data during data # acquisition and make notes of observed malfunctioning channels to # your measurement protocol sheet. # # - View the on-line averages and check the condition of the channels. # # - Compute preliminary off-line averages with artifact rejection, # SSP/ICA, and EEG average electrode reference computation # off and check the condition of the channels. # # - View raw data with :func:`mne.io.Raw.plot` without SSP/ICA # enabled and identify bad channels. # # .. note:: # Setting the bad channels should be done as early as possible in the # analysis pipeline. That's why it's recommended to set bad channels # the raw objects/files. If present in the raw data # files, the bad channel selections will be automatically transferred # to averaged files, noise-covariance matrices, forward solution # files, and inverse operator decompositions. # # The actual removal happens using :func:`pick_types ` with # `exclude='bads'` option (see :ref:`picking_channels`). ############################################################################### # Instead of removing the bad channels, you can also try to repair them. # This is done by **interpolation** of the data from other channels. # To illustrate how to use channel interpolation let us load some data. # Reading data with a bad channel marked as bad: fname = data_path + '/MEG/sample/sample_audvis-ave.fif' evoked = mne.read_evokeds(fname, condition='Left Auditory', baseline=(None, 0)) # restrict the evoked to EEG and MEG channels evoked.pick_types(meg=True, eeg=True, exclude=[]) # plot with bads evoked.plot(exclude=[], time_unit='s') print(evoked.info['bads']) ############################################################################### # Let's now interpolate the bad channels (displayed in red above) evoked.interpolate_bads(reset_bads=False, verbose=False) ############################################################################### # Let's plot the cleaned data evoked.plot(exclude=[], time_unit='s') ############################################################################### # .. note:: # Interpolation is a linear operation that can be performed also on # Raw and Epochs objects. # # For more details on interpolation see the page :ref:`channel_interpolation`. ############################################################################### # .. _marking_bad_segments: # # Marking bad raw segments with annotations # ----------------------------------------- # # MNE provides an :class:`mne.Annotations` class that can be used to mark # segments of raw data and to reject epochs that overlap with bad segments # of data. The annotations are automatically synchronized with raw data as # long as the timestamps of raw data and annotations are in sync. # # See :ref:`sphx_glr_auto_tutorials_plot_brainstorm_auditory.py` # for a long example exploiting the annotations for artifact removal. # # The instances of annotations are created by providing a list of onsets and # offsets with descriptions for each segment. The onsets and offsets are marked # as seconds. ``onset`` refers to time from start of the data. ``offset`` is # the duration of the annotation. The instance of :class:`mne.Annotations` # can be added as an attribute of :class:`mne.io.Raw`. eog_events = mne.preprocessing.find_eog_events(raw) n_blinks = len(eog_events) # Center to cover the whole blink with full duration of 0.5s: onset = eog_events[:, 0] / raw.info['sfreq'] - 0.25 duration = np.repeat(0.5, n_blinks) annot = mne.Annotations(onset, duration, ['bad blink'] * n_blinks, orig_time=raw.info['meas_date']) raw.set_annotations(annot) print(raw.annotations) # to get information about what annotations we have raw.plot(events=eog_events) # To see the annotated segments. ############################################################################### # It is also possible to draw bad segments interactively using # :meth:`raw.plot ` (see # :ref:`sphx_glr_auto_tutorials_plot_visualize_raw.py`). # # As the data is epoched, all the epochs overlapping with segments whose # description starts with 'bad' are rejected by default. To turn rejection off, # use keyword argument ``reject_by_annotation=False`` when constructing # :class:`mne.Epochs`. When working with neuromag data, the ``first_samp`` # offset of raw acquisition is also taken into account the same way as with # event lists. For more see :class:`mne.Epochs` and :class:`mne.Annotations`. ############################################################################### # .. _rejecting_bad_epochs: # # Rejecting bad epochs # -------------------- # # When working with segmented data (Epochs) MNE offers a quite simple approach # to automatically reject/ignore bad epochs. This is done by defining # thresholds for peak-to-peak amplitude and flat signal detection. # # In the following code we build Epochs from Raw object. One of the provided # parameter is named *reject*. It is a dictionary where every key is a # channel type as a string and the corresponding values are peak-to-peak # rejection parameters (amplitude ranges as floats). Below we define # the peak-to-peak rejection values for gradiometers, # magnetometers and EOG: reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6) ############################################################################### # .. note:: # The rejection values can be highly data dependent. You should be careful # when adjusting these values. Make sure not too many epochs are rejected # and look into the cause of the rejections. Maybe it's just a matter # of marking a single channel as bad and you'll be able to save a lot # of data. ############################################################################### # We then construct the epochs events = mne.find_events(raw, stim_channel='STI 014') event_id = {"auditory/left": 1} tmin = -0.2 # start of each epoch (200ms before the trigger) tmax = 0.5 # end of each epoch (500ms after the trigger) baseline = (None, 0) # means from the first instant to t = 0 picks_meg = mne.pick_types(raw.info, meg=True, eeg=False, eog=True, stim=False, exclude='bads') epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks_meg, baseline=baseline, reject=reject, reject_by_annotation=True) ############################################################################### # We then drop/reject the bad epochs epochs.drop_bad() ############################################################################### # And plot the so-called *drop log* that details the reason for which some # epochs have been dropped. print(epochs.drop_log[40:45]) # only a subset epochs.plot_drop_log() ############################################################################### # What you see is that some drop log values are empty. It means event was kept. # If it says 'IGNORED' is means the event_id did not contain the associated # event. If it gives the name of channel such as 'EOG 061' it means the # epoch was rejected because 'EOG 061' exceeded the peak-to-peak rejection # limit.