mne.preprocessing.compute_proj_eog#

mne.preprocessing.compute_proj_eog(raw, raw_event=None, tmin=-0.2, tmax=0.2, n_grad=2, n_mag=2, n_eeg=2, l_freq=1.0, h_freq=35.0, average=True, filter_length='10s', n_jobs=None, reject={'eeg': 0.0005, 'eog': inf, 'grad': 2e-10, 'mag': 3e-12}, flat=None, bads=[], avg_ref=False, no_proj=False, event_id=998, eog_l_freq=1, eog_h_freq=10, tstart=0.0, filter_method='fir', iir_params=None, ch_name=None, copy=True, return_drop_log=False, meg='separate', verbose=None)[source]#

Compute SSP (signal-space projection) vectors for EOG artifacts.

This function will:

  1. Filter the EOG data channel.

  2. Find the peaks of eyeblinks in the EOG data using mne.preprocessing.find_eog_events().

  3. Filter the raw data.

  4. Create Epochs around the eyeblinks.

  5. Optionally average the Epochs to produce an Evoked if average=True was passed (default).

  6. Calculate SSP projection vectors on that data to capture the artifacts.

Note

Raw data must be preloaded.

Parameters:
rawmne.io.Raw

Raw input file.

raw_eventmne.io.Raw or None

Raw file to use for event detection (if None, raw is used).

tminfloat

Time before event in seconds.

tmaxfloat

Time after event in seconds.

n_gradint

Number of SSP vectors for gradiometers.

n_magint

Number of SSP vectors for magnetometers.

n_eegint

Number of SSP vectors for EEG.

l_freqfloat | None

Filter low cut-off frequency for the data channels in Hz.

h_freqfloat | None

Filter high cut-off frequency for the data channels in Hz.

averagebool

Compute SSP after averaging. Default is True.

filter_lengthstr | int | None

Number of taps to use for filtering.

n_jobsint | None

The number of jobs to run in parallel. If -1, it is set to the number of CPU cores. Requires the joblib package. None (default) is a marker for ‘unset’ that will be interpreted as n_jobs=1 (sequential execution) unless the call is performed under a joblib.parallel_backend() context manager that sets another value for n_jobs.

rejectdict | None

Epoch rejection configuration (see Epochs).

flatdict | None

Epoch flat configuration (see Epochs).

badslist

List with (additional) bad channels.

avg_refbool

Add EEG average reference proj.

no_projbool

Exclude the SSP projectors currently in the fiff file.

event_idint

ID to use for events.

eog_l_freqfloat

Low pass frequency applied to the E0G channel for event detection.

eog_h_freqfloat

High pass frequency applied to the EOG channel for event detection.

tstartfloat

Start artifact detection after tstart seconds.

filter_methodstr

Method for filtering (‘iir’ or ‘fir’).

iir_paramsdict | None

Dictionary of parameters to use for IIR filtering. See mne.filter.construct_iir_filter for details. If iir_params is None and method=”iir”, 4th order Butterworth will be used.

ch_namestr | None

If not None, specify EOG channel name.

copybool

If False, filtering raw data is done in place. Defaults to True.

return_drop_logbool

If True, return the drop log.

New in version 0.15.

megstr

Can be ‘separate’ (default) or ‘combined’ to compute projectors for magnetometers and gradiometers separately or jointly. If ‘combined’, n_mag == n_grad is required and the number of projectors computed for MEG will be n_mag.

New in version 0.18.

verbosebool | str | int | None

Control verbosity of the logging output. If None, use the default verbosity level. See the logging documentation and mne.verbose() for details. Should only be passed as a keyword argument.

Returns:
projslist of Projection

List of computed projection vectors.

eog_events: ndarray

Detected EOG events.

drop_loglist

The drop log, if requested.

Notes

Filtering is applied to the EOG channel while finding events using eog_l_freq and eog_h_freq, and then to the raw instance using l_freq and h_freq before creation of the epochs used to create the projectors.

Examples using mne.preprocessing.compute_proj_eog#

Working with CTF data: the Brainstorm auditory dataset

Working with CTF data: the Brainstorm auditory dataset

Repairing artifacts with SSP

Repairing artifacts with SSP