HED search guide

Many analysis methods locate event markers with specified properties and extract sections of the data surrounding these markers for analysis. This extraction process is called epoching or trial selection.

Analysis may also exclude data surrounding particular event markers.

Other approaches find sections of the data with particular signal characteristics and then determine which types of event markers are more likely to be associated with data sections having these characteristics.

At a more global level, analysts may want to locate datasets whose event markers have certain properties in choosing data for initial analysis or for comparisons with their own data.

Datasets whose event markers are annotated with HED (Hierarchical Event Descriptors) can be searched in a dataset independent manner. The Python HEDTools support two types of search: object-based and text-based. The object-based search can distinguish complex tag relationships as part of the search. The text-based search operates on strings rather than HED objects and is considerably faster, but less powerful. Text-based searches need the long-form of the HED strings to detect children based on a parent tag.

Where can HED search be used?

The HED search facility allows users to form sophisticated queries based on HED annotations in a dataset-independent manner. These queries can be used to locate data sets satisfying the specified criteria and to find the relevant event markers in that data.

For example, the factor_hed_tags operation of the HED remodeling tools creates factor vectors for selecting events satisfying general HED queries.

The HED-based epoching tools in EEGLAB can use HED-based search to epoch data based on HED tags.

Work is underway to integrate HED-based search into other tools including Fieldtrip and MNE-python as well into the analysis platforms NEMAR and EEGNET