Epoching

rename_events module-attribute

Python

rename_events = dict()

A dictionary specifying which events in the BIDS dataset to rename upon loading, and before processing begins.

Pass an empty dictionary to not perform any renaming.

Example

Rename audio_left in the BIDS dataset to audio/left in the pipeline:

Python

rename_events = {'audio_left': 'audio/left'}

on_rename_missing_events module-attribute

Python

on_rename_missing_events = 'raise'

How to handle the situation where you specified an event to be renamed via rename_events, but this particular event is not present in the data. By default, we will raise an exception to avoid accidental mistakes due to typos; however, if you're sure what you're doing, you may change this to 'warn' to only get a warning instead, or 'ignore' to ignore it completely.

event_repeated module-attribute

Python

event_repeated = 'error'

How to handle repeated events. We call events "repeated" if more than one event occurred at the exact same time point. Currently, MNE-Python cannot handle this situation gracefully when trying to create epochs, and will throw an error. To only keep the event of that time point ("first" here referring to the order that events appear in *_events.tsv), pass 'drop'. You can also request to create a new type of event by merging repeated events by setting this to 'merge'.

Warning

The 'merge' option is entirely untested in the MNE BIDS Pipeline as of April 1st, 2021.

conditions module-attribute

Python

conditions = None

The time-locked events based on which to create evoked responses. This can either be name of the experimental condition as specified in the BIDS *_events.tsv file; or the name of condition groups, if the condition names contain the (MNE-specific) group separator, /. See the Subselecting epochs tutorial for more information.

Passing a dictionary allows to assign a name to map a complex condition name (value) to a more legible one (value).

This is a required parameter in the configuration file, unless you are processing resting-state data. If left as None and task_is_rest is not True, we will raise an error.

Example

Specifying conditions as lists of strings:

Python

conditions = ['auditory/left', 'visual/left']
conditions = ['auditory/left', 'auditory/right']
conditions = ['auditory']  # All "auditory" conditions (left AND right)
conditions = ['auditory', 'visual']
conditions = ['left', 'right']
conditions = None  # for a resting-state analysis

Pass a dictionary to define a mapping: ```python conditions = {'simple_name': 'complex/condition/with_subconditions'} conditions = {'correct': 'response/correct', 'incorrect': 'response/incorrect'}

epochs_tmin module-attribute

Python

epochs_tmin = -0.2

The beginning of an epoch, relative to the respective event, in seconds.

Example

Python

epochs_tmin = -0.2  # 200 ms before event onset

epochs_tmax module-attribute

Python

epochs_tmax = 0.5

The end of an epoch, relative to the respective event, in seconds.

Example

Python

epochs_tmax = 0.5  # 500 ms after event onset

baseline module-attribute

Python

baseline = (None, 0)

Specifies which time interval to use for baseline correction of epochs; if None, no baseline correction is applied.

Example

Python

baseline = (None, 0)  # beginning of epoch until time point zero

epochs_metadata_tmin module-attribute

Python

epochs_metadata_tmin = None

The beginning of the time window for metadata generation, in seconds, relative to the time-locked event of the respective epoch. This may be less than or larger than the epoch's first time point. If None, use the first time point of the epoch.

epochs_metadata_tmax module-attribute

Python

epochs_metadata_tmax = None

Same as epochs_metadata_tmin, but specifying the end of the time window for metadata generation.

epochs_metadata_keep_first module-attribute

Python

epochs_metadata_keep_first = None

Event groupings using hierarchical event descriptors (HEDs) for which to store the time of the first occurrence of any event of this group in a new column with the group name, and the type of that event in a column named after the group, but with a first_ prefix. If None (default), no event aggregation will take place and no new columns will be created.

Example

Assume you have two response events types, response/left and response/right; in some trials, both responses occur, because the participant pressed both buttons. Now, you want to keep the first response only. To achieve this, set

Python

epochs_metadata_keep_first = ['response']

This will add two new columns to the metadata: response, indicating the time relative to the time-locked event; and first_response, depicting the type of event ('left' or 'right').

You may also specify a grouping for multiple event types:

Python

epochs_metadata_keep_first = ['response', 'stimulus']

This will add the columns response, first_response, stimulus, and first_stimulus.

epochs_metadata_keep_last module-attribute

Python

epochs_metadata_keep_last = None

Same as epochs_metadata_keep_first, but for keeping the last occurrence of matching event types. The columns indicating the event types will be named with a last_ instead of a first_ prefix.

epochs_metadata_query module-attribute

Python

epochs_metadata_query = None

A [metadata query][https://mne.tools/stable/auto_tutorials/epochs/30_epochs_metadata.html] specifying which epochs to keep. If the query fails because it refers to an unknown metadata column, a warning will be emitted and all epochs will be kept.

Example

Only keep epochs without a response_missing event:

Python

epochs_metadata_query = ['response_missing.isna()']

rest_epochs_duration module-attribute

Python

rest_epochs_duration = None

Duration of epochs in seconds.

rest_epochs_overlap module-attribute

Python

rest_epochs_overlap = None

Overlap between epochs in seconds. This is used if the task is 'rest' and when the annotations do not contain any stimulation or behavior events.