DS004408#

EEG responses to continuous naturalistic speech

Access recordings and metadata through EEGDash.

Citation: Giovanni M Di Liberto, Michael P Broderick, Ole Bialas, Edmund C Lalor (2023). EEG responses to continuous naturalistic speech. 10.18112/openneuro.ds004408.v1.0.8

Modality: eeg Subjects: 19 Recordings: 1946 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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},
}

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.

Dataset Information#

Dataset ID	`DS004408`
Title	EEG responses to continuous naturalistic speech
Year	2023
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

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 19
Recordings: 1946
Tasks: 1

Channels & sampling rate

Channels: 128
Sampling rate (Hz): 512.0
Duration (hours): 0.0

API Reference#

Use the DS004408 class to access this dataset programmatically.

class eegdash.dataset.DS004408(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004408. Modality: eeg; Experiment type: Other; Subject type: Healthy. 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

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None