ndx-hed 0.2.0

NWB extension for HED data

ndx-hed extension for NWB

Neurodata Without Borders (NWB) is a data standard for organizing neurophysiology data. NWB is used extensively as the data representation for single cell and animal recordings as well as human neuroimaging modalities such as IEEG. NWB organizes all of the data from one recording session into a single file.

HED (Hierarchical Event Descriptors) is a system of standardized vocabularies and supporting tools that allows fine-grained annotation of data. HED annotations can now be used in NWB to provide HED annotations for any NWB dynamic table.

The HED annotation in NWB user guide explains in more detail how to use this extension for HED.

Installation

Python:

pip install -U ndx-hed

Matlab: The Matlab extension is under development.

Quick start

from pynwb import NWBFile
from ndx_hed import HedLabMetaData, HedTags
from datetime import datetime

# Create NWB file with HED metadata
nwbfile = NWBFile(
    session_description="Example session with HED annotations",
    identifier="example_session_001",
    session_start_time=datetime.now()
)

# Add HED schema metadata (required)
hed_metadata = HedLabMetaData(hed_schema_version="8.4.0")
nwbfile.add_lab_meta_data(hed_metadata)

# Add HED column to trials table
nwbfile.add_trial_column(
    name="HED",
    col_cls=HedTags,
    data=[],
    description="HED annotations for trials"
)

# Add trials with HED annotations
nwbfile.add_trial(
    start_time=0.0,
    stop_time=1.0,
    HED="Experimental-trial, (Sensory-event, Visual-presentation)"
)

Main classes

The ndx-hed extension provides three main classes:

Class	Purpose	Use Cases
HedLabMetaData	HED schema specification and lab-specific definitions	Required for all HED validation
HedTags	Row-specific HED annotations	Row-specific tags in any DynamicTable
HedValueVector	Column-wide HED templates	Shared annotations with value placeholders (#)

Examples

The examples/ directory contains comprehensive runnable examples:

• 01_basic_hed_classes.py - Introduction to the three main classes
• 02_trials_with_hed.py - Adding HED to trials table
• 03_events_table_integration.py - Integration with ndx-events EventsTable
• 04_bids_conversion.py - Converting BIDS events to NWB with HED
• 05_hed_validation.py - Comprehensive validation examples
• 06_complete_workflow.py - End-to-end workflow demonstration
• 07_hed_definitions.py - Custom HED definitions usage

Run any example:

cd examples
python 01_basic_hed_classes.py

Or run all examples:

cd examples
python run_all_examples.py

Integration with NWB events

The ndx-hed extension works seamlessly with the ndx-events extension to provide comprehensive event annotation capabilities. HED annotations can be incorporated in three ways:

1. Direct HED column - Event-specific annotations
2. HedValueVector columns - Shared annotations with value placeholders
3. Categorical columns with MeaningsTable - Category-based annotations

See examples/03_events_table_integration.py for detailed demonstrations.

BIDS compatibility

BIDS (Brain Imaging Data Structure) is a data standard for organizing neuroimaging and behavioral data from an entire experiment. The standard uses JSON files called "sidecars" to store metadata associated with its tabular files.

The ndx-hed extension provides utilities to convert between BIDS events files and NWB EventsTable format:

from ndx_hed.utils.bids2nwb import extract_meanings, get_events_table, get_bids_events
import pandas as pd
import json

# Convert BIDS to EventsTable
bids_events_file_path = "Your_events_path_here.tsv"
bids_sidecar_file_path = "Your_json_sidecar_path_here.json"
events_df = pd.read_csv(bids, sep="\t")
json_data = json.load(bids_sidecar_file_path)
meanings = extract_meanings(bids_sidecar_file_path)
events_table = get_events_table("task_events", "Task events", events_df, meanings)

# Convert EventsTable to BIDS
bids_df, sidecar_dict = get_bids_events(events_table)

See examples/04_bids_conversion.py for complete examples.

HED validation

Creating HED annotations for NWB data and saving these annotations as part of an NWBFile does not mean the annotations are valid. HED validation is performed to ensure they conform to the HED schema:

from ndx_hed.utils.hed_nwb_validator import HedNWBValidator

# Create validator and validate entire file
hed_metadata = HedLabMetadata(hed_schema_version= '8.4.0')
validator = HedNWBValidator(hed_metadata)

# Assume nwbfile has already been created
issues = validator.validate_file(nwbfile)

if not issues:
    print("All HED annotations are valid!")

See examples/05_hed_validation.py for comprehensive validation examples.

Additional Resources

• HED specification
• HED python tools
• HED annotation in NWB user guide
• NWB documentation
• ndx-events extension

Contributing

Contributions are welcome! Feel free to submit issues or pull requests.