create_ecg_epochs(raw, ch_name=None, event_id=999, picks=None, tmin=- 0.5, tmax=0.5, l_freq=8, h_freq=16, reject=None, flat=None, baseline=None, preload=True, keep_ecg=False, reject_by_annotation=True, decim=1, verbose=None)¶
Conveniently generate epochs around ECG artifact events.
This function will:
Filter the ECG data channel.
Find ECG R wave peaks using
Filter the raw data.
Epochsaround the R wave peaks, capturing the heartbeats.
Filtering is only applied to the ECG channel while finding events. The resulting
ecg_epochswill have no filtering applied (i.e., have the same filter properties as the input
- rawinstance of
The raw data.
The name of the channel to use for ECG peak detection. If
None(default), ECG channel is used if present. If
Noneand no ECG channel is present, a synthetic ECG channel is created from the cross-channel average. This synthetic channel can only be created from MEG channels.
The index to assign to found ECG events.
Channels to include. Slices and lists of integers will be interpreted as channel indices. In lists, channel type strings (e.g.,
['meg', 'eeg']) will pick channels of those types, channel name strings (e.g.,
['MEG0111', 'MEG2623']will pick the given channels. Can also be the string values “all” to pick all channels, or “data” to pick data channels. None (default) will pick all channels. Note that channels in
info['bads']will be included if their names or indices are explicitly provided.
Start time before event.
End time after event.
Low pass frequency to apply to the ECG channel while finding events.
High pass frequency to apply to the ECG channel while finding events.
Reject epochs based on peak-to-peak signal amplitude (PTP), i.e. the absolute difference between the lowest and the highest signal value. In each individual epoch, the PTP is calculated for every channel. If the PTP of any one channel exceeds the rejection threshold, the respective epoch will be dropped.
The dictionary keys correspond to the different channel types; valid keys are:
reject = dict(grad=4000e-13, # unit: T / m (gradiometers) mag=4e-12, # unit: T (magnetometers) eeg=40e-6, # unit: V (EEG channels) eog=250e-6 # unit: V (EOG channels) )
Since rejection is based on a signal difference calculated for each channel separately, applying baseline correction does not affect the rejection procedure, as the difference will be preserved.
None(default), no rejection is performed.
Rejection parameters based on flatness of signal. Valid keys are
'ecg'. The values are floats that set the minimum acceptable peak-to-peak amplitude (PTP). If the PTP is smaller than this threshold, the epoch will be dropped. If
Nonethen no rejection is performed based on flatness of the signal.
tupleof length 2
The time interval to consider as “baseline” when applying baseline correction. If
None, do not apply baseline correction. If a tuple
(a, b), the interval is between
b(in seconds), including the endpoints. If
None, the beginning of the data is used; and if
None, it is set to the end of the interval. If
(None, None), the entire time interval is used.
(a, b)includes both endpoints, i.e. all timepoints
a <= t <= b.
Correction is applied to each epoch and channel individually in the following way:
Calculate the mean signal of the baseline period.
Subtract this mean from the entire epoch.
Preload epochs or not (default True). Must be True if keep_ecg is True.
When ECG is synthetically created (after picking), should it be added to the epochs? Must be False when synthetic channel is not used. Defaults to False.
Whether to reject based on annotations. If
True(default), epochs overlapping with segments whose description begins with
'bad'are rejected. If
False, no rejection based on annotations is performed.
New in version 0.14.0.
Factor by which to subsample the data.
Low-pass filtering is not performed, this simply selects every Nth sample (where N is the value passed to
decim), i.e., it compresses the signal (see Notes). If the data are not properly filtered, aliasing artifacts may occur.
New in version 0.21.0.
- rawinstance of
- ecg_epochsinstance of
Data epoched around ECG R wave peaks.
- ecg_epochsinstance of