regular_epochs.RegularEpochsMixin

class autoclean.mixins.signal_processing.regular_epochs.RegularEpochsMixin

Mixin class providing regular (fixed-length) epochs creation functionality for EEG data.

create_regular_epochs(data=None, tmin=-1, tmax=1, baseline=None, volt_threshold=None, stage_name='post_epochs', reject_by_annotation=False, export=False)

Create regular fixed-length epochs from raw data.

Parameters:

datamne.io.Raw, Optional

The raw data to create epochs from. If None, uses self.raw.

tminfloat, Optional

The start time of the epoch in seconds. Default is -1.

tmaxfloat, Optional

The end time of the epoch in seconds. Default is 1.

baselinetuple of float, Optional

The time interval to apply baseline correction. Default is None.

volt_thresholddict, Optional

Dictionary of channel types and thresholds for rejection, by default None.

stage_namestr, Optional

Name for saving and metadata tracking. Default is “post_epochs”.

reject_by_annotationbool, Optional

Whether to automatically reject epochs that overlap with bad annotations, or just mark them in the metadata for later processing. Default is False.

exportbool, Optional

If True, exports the processed epochs to the stage directory. Default is False.

Returns:

epochs_clean: mne.Epochs

The created epochs object with bad epochs marked (and dropped if reject_by_annotation=True)

See also

create_eventid_epochs

For creating epochs based on specific event markers.

Notes

If reject_by_annotation is False, an intermediate file with bad epochs marked but not dropped is saved.

The epoching parameters can be customized through the configuration file (autoclean_config.yaml) under the “epoch_settings” section. If enabled, the configuration values will override the default parameters.