EEGdash›OpenNeuro›DS004408
Iss. 4408 · 19 subjects · 380 recordings · CC0
Dataset Brief · EEG responses to continuous naturalistic speech

DS004408: eeg dataset, 19 subjects#

EEG responses to continuous naturalistic speech

Citation: Giovanni M Di Liberto, Michael P Broderick, Ole Bialas, Edmund C Lalor (2015). EEG responses to continuous naturalistic speech. 10.18112/openneuro.ds004408.v1.0.8

19-participant EEG dataset — EEG responses to continuous naturalistic speech.

Data & curation Giovanni M Di Liberto · Michael P Broderick · Ole Bialas · Edmund C Lalor
Year 2015 · Distributed via OpenNeuro
EEG · 128 ch512 HzBIDS 1.7.0Task · listeningHealthyAuditoryOther
Layer 01Study
What was asked
Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.
Layer 02Signal · BIDS
What was recorded
Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.
Layer 03Training · ML
What you can train on
Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.
§ 01Access · Get started

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004408

dataset = DS004408(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004408(cache_dir="./data", subject="01")

Advanced query

dataset = DS004408(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds004408,
  title = {EEG responses to continuous naturalistic speech},
  author = {Giovanni M Di Liberto and Michael P Broderick and Ole Bialas and Edmund C Lalor},
  doi = {10.18112/openneuro.ds004408.v1.0.8},
  url = {https://doi.org/10.18112/openneuro.ds004408.v1.0.8},
}
§ 02Study · The README

About This Dataset#

The data in one study [^1] and then added to by another [^2] and contains EEG responses of healthy, neurotypical adults who listened to naturalistic speech. The subjects listened to segments from an audio book version of “The Old Man and the Sea” and their brain activity was recorded using a 128-channel ActiveTwo EEG system (BioSemi).

The stimuli folder contains .wav files of the presented audiobook segments as well as a .TextGrid file for each segment, containng the timing of words and phonemes in that segment. The text grids were generated using the forced-alignment software Prosodylab-Aligner [^3] and inspected by eye. Each subject’s folder contains one EEG-recording per audio segment and their starts are aligned (the EEG recordings are longer than the audio to a varying extent). The recordings are unfiltered, unreferenced and sampled at 512 Hz.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 128 ch (n=380 recordings)

Sampling frequencies: 512.0 Hz (n=380 recordings)

Total recording duration: 20 h 35 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 128 ch · EEG · 512 Hz · 19 subjects, 380 recordings
Live trace viewer — sub-019 · task-listening · run-09

Showing one representative recording out of 19 subjects and 380 recordings in this dataset. Browse the full set on OpenNeuro; drop any other _eeg.{set,edf,bdf,vhdr} file onto the viewer (or pass ?eeg=<url>) to inspect it.

Electrode layout — EEG · 128 sensors — 128 channels

NEMAR Processing Statistics#

The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.

NEMAR pipeline statistics — DS004408
HED event descriptors word cloud HED event descriptors word cloud — DS004408
§ 05Manifest · BIDS tree

Manifest#

File Explorer#

Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.

Recordings—
Files—
Subjects—
Modalities—
Click to load file structure…
Full dataset metadata table

Dataset ID

DS004408

Title

EEG responses to continuous naturalistic speech

Author (year)

Liberto2023

Canonical

—

Importable as

DS004408, Liberto2023

Year

2015

Authors

Giovanni M Di Liberto, Michael P Broderick, Ole Bialas, Edmund C Lalor

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004408.v1.0.8

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004408,
  title = {EEG responses to continuous naturalistic speech},
  author = {Giovanni M Di Liberto and Michael P Broderick and Ole Bialas and Edmund C Lalor},
  doi = {10.18112/openneuro.ds004408.v1.0.8},
  url = {https://doi.org/10.18112/openneuro.ds004408.v1.0.8},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS004408(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Liberto2023
Canonical—
Importable asDS004408 · Liberto2023
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS004408(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

EEG responses to continuous naturalistic speech

Study:

ds004408 (OpenNeuro)

Author (year):

Liberto2023

Canonical:

—

Also importable as: DS004408, Liberto2023.

Modality: eeg. Subjects: 19; recordings: 380; tasks: 1.

Parameters:

  • cache_dir (str | Path) – Directory where data are cached locally.

  • query (dict | None) – Additional MongoDB-style filters to AND with the dataset selection. Must not contain the key dataset.

  • s3_bucket (str | None) – Base S3 bucket used to locate the data.

  • **kwargs (dict) – Additional keyword arguments forwarded to EEGDashDataset.

data_dir#

Local dataset cache directory (cache_dir / dataset_id).

Type:

Path

query#

Merged query with the dataset filter applied.

Type:

dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:

list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds004408 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004408 DOI: https://doi.org/10.18112/openneuro.ds004408.v1.0.8 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004408
>>> dataset = DS004408(cache_dir="./data")
>>> recording = dataset[0]
>>> raw = recording.load()
__init__(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
save(path: str, overwrite: bool = False, offset: int = 0)[source]#

Save datasets to files by creating one subdirectory for each dataset:

path/
    0/
        0-raw.fif | 0-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
    1/
        1-raw.fif | 1-epo.fif
        description.json
        raw_preproc_kwargs.json (if raws were preprocessed)
        window_kwargs.json (if this is a windowed dataset)
        window_preproc_kwargs.json  (if windows were preprocessed)
        target_name.json (if target_name is not None and dataset is raw)
Parameters:

  • path (str) –

    Directory in which subdirectories are created to store

    -raw.fif | -epo.fif and .json files to.

  • overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.

  • offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML
.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne
BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode
DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch
Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr
Hugging FacePre-bundled mirror at EEGDash/ds004408 · pull with datasets.load_dataset("EEGDash/ds004408").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS004408.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons
Examples using EEGDashcurated · start here
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.Load one EEG recordingResolve a single record to an MNE Raw with channels and events.EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for ds004408 to reproduce the tutorial on this dataset.

Citation

Giovanni M Di Liberto, Michael P Broderick, Ole Bialas, Edmund C Lalor (2015). EEG responses to continuous naturalistic speech. 10.18112/openneuro.ds004408.v1.0.8

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds004408.v1.0.8.

BIDS
BIDS 1.7.0
Sidecars
channels · eeg.json
Machine-readable
schema.org/Dataset · Croissant

See Also#