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:

Filter the EOG data channel.
Find the peaks of eyeblinks in the EOG data using mne.preprocessing.find_eog_events().
Filter the raw data.
Create Epochs around the eyeblinks.
Optionally average the Epochs to produce an Evoked if average=True was passed (default).
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_config 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 v0.15.
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 v0.18.
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.

Examples using `mne.preprocessing.compute_proj_eog`#

Working with CTF data: the Brainstorm auditory dataset

Repairing artifacts with SSP

mne.preprocessing.compute_proj_eog#

Examples using mne.preprocessing.compute_proj_eog#

Examples using `mne.preprocessing.compute_proj_eog`#