NM000238: eeg dataset, 87 subjects#

Name: SparrKULee: A Speech-Evoked Auditory Response Repository from KU Leuven, Containing the EEG of 85 Participants
Published: 2024-01-01
License: Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) for the EEG data. Stimuli can only be used for non-commercial purposes.

SparrKULee: A Speech-Evoked Auditory Response Repository from KU Leuven, Containing the EEG of 85 Participants

Access recordings and metadata through EEGDash.

Citation: Bernd Accou, Lies Bollens, Marlies Gillis, Wendy Verheijen, Hugo Van hamme, Tom Francart (2024). SparrKULee: A Speech-Evoked Auditory Response Repository from KU Leuven, Containing the EEG of 85 Participants. 10.82901/nemar.nm000238

Modality: eeg Subjects: 87 Recordings: 4088 License: Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) for the EEG data. Stimuli can only be used for non-commercial purposes. Source: nemar

Metadata: Good (80%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000238

dataset = NM000238(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 = NM000238(cache_dir="./data", subject="01")

Advanced query

dataset = NM000238(
    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{nm000238,
  title = {SparrKULee: A Speech-Evoked Auditory Response Repository from KU Leuven, Containing the EEG of 85 Participants},
  author = {Bernd Accou and Lies Bollens and Marlies Gillis and Wendy Verheijen and Hugo Van hamme and Tom Francart},
  doi = {10.82901/nemar.nm000238},
  url = {https://doi.org/10.82901/nemar.nm000238},
}

About This Dataset#

DOI

IMPORTANT — RESTRICTED SUBJECTS EXCLUDED FROM NEMAR RE-HOST

IMPORTANT — 5 of the 85 original subjects (sub-019, sub-020, sub-021, sub-022, sub-026) are EXCLUDED from this NEMAR re-host because their raw EEG files are access-restricted on the KU Leuven Dataverse (HTTP 403 on download without a data-use agreement). Researchers who need these subjects should email sparrkulee@kuleuven.be to request access and download the data directly from https://rdr.kuleuven.be/dataset.xhtml?persistentId=doi:10.48804/K3VSND (DOI 10.48804/K3VSND). The re-host therefore contains 80 of the original 85 subjects, covering all 11 session types (shortstories01, varyingStories01..10).

Excluded subjects: sub-019, sub-020, sub-021, sub-022, sub-026

View full README

DOI

IMPORTANT — RESTRICTED SUBJECTS EXCLUDED FROM NEMAR RE-HOST

IMPORTANT — 5 of the 85 original subjects (sub-019, sub-020, sub-021, sub-022, sub-026) are EXCLUDED from this NEMAR re-host because their raw EEG files are access-restricted on the KU Leuven Dataverse (HTTP 403 on download without a data-use agreement). Researchers who need these subjects should email sparrkulee@kuleuven.be to request access and download the data directly from https://rdr.kuleuven.be/dataset.xhtml?persistentId=doi:10.48804/K3VSND (DOI 10.48804/K3VSND). The re-host therefore contains 80 of the original 85 subjects, covering all 11 session types (shortstories01, varyingStories01..10).

Excluded subjects: sub-019, sub-020, sub-021, sub-022, sub-026

Cohort demographics

Cohort demographics (from Accou et al., Data 2024, 9, 94, Section 2.1): 85 original participants, 74 female / 11 male, aged 21.4 ± 1.9 years (mean ± SD), inclusion window 18-30 years, all normal-hearing (≤30 dB HL, 125-8000 Hz), native Dutch/Flemish speakers. Per-subject numeric ages are not published by the SparrKULee authors for privacy reasons; participants.tsv only ships 3-year binned ages in the age_range column (see participants.json for details).

How to cite

Please cite the original SparrKULee data descriptor when using this dataset: Accou, B., Bollens, L., Gillis, M., Verheijen, W., Van hamme, H., & Francart, T. (2024). SparrKULee: A Speech-Evoked Auditory Response Repository from KU Leuven, Containing the EEG of 85 Participants. Data, 9(8), 94. https://doi.org/10.3390/data9080094

Where extra metadata lives (after NEMAR preparation)

/code/task-listeningActive_eeg.json — full recording-level EEG metadata (SamplingFrequency, Manufacturer, EEGChannelCount, EEGReference, PowerLineFrequency, …). Relocated from the dataset root because the validator does not match the orphan top-level sidecar against the .bdf.gz data files.

/code/remarks/ — per-session free-form recording notes (.txt and .docx) originally placed under sub-XX/ses-YY/remarks/. Relocated so the validator does not see an arbitrary remarks/ folder inside BIDS session directories.

/code/convert_accou2023.py — the exact script that was run to produce this NEMAR re-host.

README

__SparrKULee__: A Speech-evoked Auditory Response Repository of the KU Leuven, containing EEG of 85 participants

Overview

An overview of the dataset including details about the filetypes, methods and technical validation can be found in [our paper]()

Notes

Code to download, preprocess and validate the data can be found at exporl/auditory-eeg-dataset.

Due to mistakes during recording, following recordings do not have an adequate number of triggers and can therefore not be accurately aligned with the stimulus:

sub-006/ses-shortstories01/eeg/sub-006_ses-shortstories01_task-listeningActive_run-06_eeg.bdf.gz
sub-017/ses-shortstories01/eeg/sub-017_ses-shortstories01_task-listeningActive_run-03_eeg.bdf.gz
sub-048/ses-varyingStories05/eeg/sub-048_ses-varyingStories05_task-listeningActive_run-04_eeg.bdf.gz

Dataset Information#

Dataset ID	`NM000238`
Title	SparrKULee: A Speech-Evoked Auditory Response Repository from KU Leuven, Containing the EEG of 85 Participants
Author (year)	`Accou2024`
Canonical	—
Importable as	`NM000238`, `Accou2024`
Year	2024
Authors	Bernd Accou, Lies Bollens, Marlies Gillis, Wendy Verheijen, Hugo Van hamme, Tom Francart
License	Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) for the EEG data. Stimuli can only be used for non-commercial purposes.
Citation / DOI	10.82901/nemar.nm000238
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: 87
Recordings: 4088
Tasks: 366

Channels & sampling rate

Channels: Varies
Sampling rate (Hz): Varies
Duration (hours): Not calculated

Electrode Layout#

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

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.

HED event descriptors word cloud

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the NM000238 class to access this dataset programmatically.

class eegdash.dataset.NM000238(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

SparrKULee: A Speech-Evoked Auditory Response Repository from KU Leuven, Containing the EEG of 85 Participants

Study:: nm000238 (NeMAR)
Author (year):: Accou2024
Canonical:: —

Also importable as: NM000238, Accou2024.

Modality: eeg. Subjects: 87; recordings: 4088; tasks: 366.

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/nm000238 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000238 DOI: https://doi.org/10.82901/nemar.nm000238

Examples

>>> from eegdash.dataset import NM000238
>>> dataset = NM000238(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)