EEGdash›OpenNeuro›DS003420

Iss. 3420 · 23 subjects · 92 recordings · CC0

Dataset Brief · HD-EEGtask(Dataset 1)

DS003420: eeg dataset, 23 subjects#

HD-EEGtask(Dataset 1)

Citation: Ahmad Mheich, Olivier Dufor, Sahar Yassine, Aya Kabbara, Arnaud Biraben, Fabrice Wendling, Mahmoud Hassan (20). HD-EEGtask(Dataset 1). 10.18112/openneuro.ds003420.v1.0.2

23-participant EEG dataset — HD-EEGtask(Dataset 1).

Data & curation Ahmad Mheich · Olivier Dufor · Sahar Yassine · Aya Kabbara · Arnaud Biraben · Fabrice Wendling · …
Year 20 · Distributed via OpenNeuro

EEG · 256 (80), 257 (12) ch1000 HzBIDS 1.22 sessionsHealthyVisualOther

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#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003420

dataset = DS003420(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 = DS003420(cache_dir="./data", subject="01")

Advanced query

dataset = DS003420(
    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{ds003420,
  title = {HD-EEGtask(Dataset 1)},
  author = {Ahmad Mheich and Olivier Dufor and Sahar Yassine and Aya Kabbara and Arnaud Biraben and Fabrice Wendling and Mahmoud Hassan},
  doi = {10.18112/openneuro.ds003420.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds003420.v1.0.2},
}

§ 02Study · The README

About This Dataset#

This dataset was collected between 2012 and 2013 in Rennes (France) during two conditions (visual naming and spelling tasks).

The dataset consists of naming and spelling the names of visually presented objects. The data was collected in the Rennes University Hospital. This experiment was approved by an independent ethics committee and authorized by the French institutional review board (IRB): “Comite de Protection des Personnes dans la Recherche Biomedicale Ouest V” (CCPPRB-Ouest V). This study was registered under the name “conneXion” and the agreement number: 2012- A01227-36.

Twenty-three right-handed healthy volunteers of whom 12 females, with an age range between

19 and 40 years (mean age 28 year),and 11 males with an age range between 19 and 33 years (mean age 23 years) participated in this study. (See participants.json and participants.tsv for more details)

Dataset 1

Presentation

Experiment

The experiment begins with the verification of inclusion/exclusion criteria.

The participants read the information notice and the consent form.

Then they sign two questionnaires.

One subject –>Two conditions (naming and spelling)–> two runs for each condition.

Each run contains 74 stimuli.

The spelling task always follow the naming task and its instruction was not given before the naming task was completed to avoid any reminiscence of words orthographic structures

Each run contains balanced numbers of animals and objects as well as long and short words.

Pictures are presented on a screen using a computer and the experimental paradigm is presented using E-prime Psychology Software Tools.

The responses produced by the participants were collected via a Logitech microphone and analyzed to detect onsets of speech using Praat v5.3.13(University of Amsterdam, 1012VT Amsterdam, The Netherlands).

EEG acquisition

HD-EEG system (EGI, Electrical Geodesic Inc., 256 electrodes)

Sampling frequency: 1000Hz

Impedances were kept below 5k

Contact

If you have any questions or comments, please contact:

Ahmad Mheich: mheich.ahmad@gmail.com

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=23, range 19–40 yr, mean 26.3 yr)

152025303540

Other · 23

Channel counts (ch)

256257

Sampling frequencies: 1000.0 Hz (n=92 recordings)

Total recording duration: 13 h 32 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 256 (80), 257 (12) ch · EEG · 1000 Hz · 23 subjects, 92 recordings

Live trace viewer — sub-13 · ses-spelling · run-1

Showing one representative recording out of 23 subjects and 92 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 · 256 sensors — 256 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.

HED event descriptors word cloud

§ 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…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.DS003420(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Mheich2020_HD

Canonical—

Importable asDS003420 · Mheich2020_HD

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.DS003420(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

HD-EEGtask(Dataset 1)

Study:: ds003420 (OpenNeuro)
Author (year):: Mheich2020_HD
Canonical:: —

Also importable as: DS003420, Mheich2020_HD.

Modality: eeg; Experiment type: Other; Subject type: Healthy. Subjects: 23; recordings: 92; tasks: 0.

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/ds003420 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003420 DOI: https://doi.org/10.18112/openneuro.ds003420.v1.0.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS003420
>>> dataset = DS003420(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/ds003420 · pull with datasets.load_dataset("EEGDash/ds003420").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS003420.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 ds003420 to reproduce the tutorial on this dataset.

Citation

Ahmad Mheich, Olivier Dufor, Sahar Yassine, Aya Kabbara, Arnaud Biraben, … (20). HD-EEGtask(Dataset 1). 10.18112/openneuro.ds003420.v1.0.2

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds003420.v1.0.2.

Related & sibling datasets

DS002833EEG · 20 subjSame authors DS005520EEG · 23 subj DS004745EEG · 6 subj DS005932EEG · 29 subj DS004517EEG · 7 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.2

Sidecars

events · events.json · channels · electrodes · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds003420.v1.0.2

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS003420`
Title	HD-EEGtask(Dataset 1)
Author (year)	`Mheich2020_HD`
Canonical	—
Importable as	`DS003420`, `Mheich2020_HD`
Year	20
Authors	Ahmad Mheich, Olivier Dufor, Sahar Yassine, Aya Kabbara, Arnaud Biraben, Fabrice Wendling, Mahmoud Hassan
License	CC0
Citation / DOI	10.18112/openneuro.ds003420.v1.0.2
Source links	OpenNeuro \| NeMAR \| Source URL

DS003420: eeg dataset, 23 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#