mne.preprocessing.compute_proj_ecg#

mne.preprocessing.compute_proj_ecg(raw, raw_event=None, tmin=- 0.2, tmax=0.4, n_grad=2, n_mag=2, n_eeg=2, l_freq=1.0, h_freq=35.0, average=True, filter_length='10s', n_jobs=1, ch_name=None, reject={'grad': 2e-10, 'mag': 3e-12}, flat=None, bads=[], avg_ref=False, no_proj=False, event_id=999, ecg_l_freq=5, ecg_h_freq=35, tstart=0.0, qrs_threshold='auto', filter_method='fir', iir_params=None, copy=True, return_drop_log=False, meg='separate', verbose=None)[source]#

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

This function will:

  1. Filter the ECG data channel.

  2. Find ECG R wave peaks using mne.preprocessing.find_ecg_events().

  3. Filter the raw data.

  4. Create Epochs around the R wave peaks, capturing the heartbeats.

  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 will be loaded if it hasn’t been preloaded already.

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

The number of jobs to run in parallel (default 1). If -1, it is set to the number of CPU cores. Requires the joblib package.

ch_namestr | None

Channel to use for ECG detection (Required if no ECG found).

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.

ecg_l_freqfloat

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

ecg_h_freqfloat

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

tstartfloat

Start artifact detection after tstart seconds.

qrs_thresholdfloat | str

Between 0 and 1. qrs detection threshold. Can also be “auto” to automatically choose the threshold that generates a reasonable number of heartbeats (40-160 beats / min).

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.

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
projlist

Computed SSP projectors.

ecg_eventsndarray

Detected ECG events.

drop_loglist

The drop log, if requested.

Notes

Filtering is applied to the ECG channel while finding events using ecg_l_freq and ecg_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_ecg#

Background on projectors and projections

Background on projectors and projections

Background on projectors and projections
Repairing artifacts with SSP

Repairing artifacts with SSP

Repairing artifacts with SSP
Divide continuous data into equally-spaced epochs

Divide continuous data into equally-spaced epochs

Divide continuous data into equally-spaced epochs