EEGdash›NeMAR›NM000206

Iss. 206 · 15 subjects · 30 recordings · CC-BY-SA-4.0

Dataset Brief · Neuroergonomic 2021 dataset

NM000206: eeg dataset, 15 subjects#

Neuroergonomic 2021 dataset

Citation: Marcel F. Hinss, Emilie S. Jahanpour, Bertille Somon, Lou Pluchon, Frédéric Dehais, Raphaëlle N. Roy (2023). Neuroergonomic 2021 dataset.

15-participant EEG dataset — Neuroergonomic 2021 dataset.

Data & curation Marcel F. Hinss · Emilie S. Jahanpour · Bertille Somon · Lou Pluchon · Frédéric Dehais · Raphaëlle N. Roy
Year 2023 · Distributed via NeMAR
Funding ERASMUS program · ANITI (Artificial and Natural Intelligence Toulouse Institute)

EEG · 61 ch500 HzBIDS 1.9.0Task · rstate2 sessionsHealthyVisualAttention

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 NM000206

dataset = NM000206(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 = NM000206(cache_dir="./data", subject="01")

Advanced query

dataset = NM000206(
    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{nm000206,
  title = {Neuroergonomic 2021 dataset},
  author = {Marcel F. Hinss and Emilie S. Jahanpour and Bertille Somon and Lou Pluchon and Frédéric Dehais and Raphaëlle N. Roy},
}

§ 02Study · The README

About This Dataset#

Neuroergonomic 2021 dataset.

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser

Neuroergonomic 2021 dataset

rs

├─ Experiment-structure
└─ Rest

View full README

Neuroergonomic 2021 dataset

rs

     ├─ Experiment-structure
     └─ Rest

easy

     ├─ Experiment-structure
     └─ Label/easy

medium

     ├─ Experiment-structure
     └─ Label/medium

diff

├─ Experiment-structure
└─ Label/difficult

Paradigm-Specific Parameters

Detected paradigm: resting_state

Data Structure

Trials: 90
Trials context: total

Preprocessing

Data state: raw
Preprocessing applied: False

Signal Processing

Classifiers: MDM, Riemannian
Feature extraction: Bandpower, Covariance/Riemannian, ICA
Frequency bands: alpha=[8.0, 13.0] Hz; theta=[4.0, 8.0] Hz

Cross-Validation

Method: 5-fold
Folds: 5
Evaluation type: cross_subject, cross_session, transfer_learning

Performance (Original Study)

Accuracy: 70.67%

BCI Application

Applications: neuroergonomics, mental_workload_estimation
Environment: laboratory

Tags

Pathology: Healthy
Modality: Cognitive
Type: Research

Documentation

DOI: 10.1038/s41597-022-01898-y
License: CC-BY-SA-4.0
Investigators: Marcel F. Hinss, Emilie S. Jahanpour, Bertille Somon, Lou Pluchon, Frédéric Dehais, Raphaëlle N. Roy
Senior author: Raphaëlle N. Roy
Contact: marcel.hinss@isae-supaero.fr
Institution: ISAE-SUPAERO, Université de Toulouse
Department: Department of Information Processing and Systems
Address: Toulouse, France
Country: FR
Repository: Zenodo
Data URL: https://doi.org/10.5281/zenodo.6874128
Publication year: 2023
Funding: ERASMUS program; ANITI (Artificial and Natural Intelligence Toulouse Institute)
Ethics approval: Comité d’Éthique de la Recherche (CER), Université de Toulouse (CER number 2021-342)
Acknowledgements: This research was supported in part by the ERASMUS program (which funded Mr Hinss’ internship), and by ANITI (Artificial and Natural Intelligence Toulouse Institute), Toulouse, France.
How to acknowledge: Please cite: Hinss et al. (2023). Open multi-session and multi-task EEG cognitive dataset for passive brain-computer interface applications. Scientific Data, 10, 85. https://doi.org/10.1038/s41597-022-01898-y

References

[Hinss2021] M. Hinss, B. Somon, F. Dehais & R. N. Roy (2021) Open EEG Datasets for Passive Brain-Computer Interface Applications: Lacks and Perspectives. IEEE Neural Engineering Conference. [Hinss2023] M. F. Hinss, et al. (2023) An EEG dataset for cross-session mental workload estimation: Passive BCI competition of the Neuroergonomics Conference 2021. Scientific Data, 10, 85. https://doi.org/10.1038/s41597-022-01898-y Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Hochenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8 Generated by MOABB 1.5.0 (Mother of All BCI Benchmarks) NeuroTechX/moabb

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=15, range 24–24 yr, mean 23.0 yr)

20

Other · 15

Channel counts: 61 ch (n=30 recordings)

Sampling frequencies: 500.0 Hz (n=30 recordings)

Total recording duration: 3 h 58 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 61 ch · EEG · 500 Hz · 15 subjects, 30 recordings

Live trace viewer — sub-13 · ses-2 · task-rstate · run-0

Showing one representative recording out of 15 subjects and 30 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 · 61 sensors — 61 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.NM000206(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Hinss2021_Neuroergonomic

Canonical—

Importable asNM000206 · Hinss2021_Neuroergonomic

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

class eegdash.dataset.NM000206(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Neuroergonomic 2021 dataset

Study:: nm000206 (NeMAR)
Author (year):: Hinss2021_Neuroergonomic
Canonical:: —

Also importable as: NM000206, Hinss2021_Neuroergonomic.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 15; recordings: 30; 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/nm000206 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000206

Examples

>>> from eegdash.dataset import NM000206
>>> dataset = NM000206(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 FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — NM000206.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 nm000206 to reproduce the tutorial on this dataset.

Citation

Marcel F. Hinss, Emilie S. Jahanpour, Bertille Somon, Lou Pluchon, Frédéric Dehais, … (2023). Neuroergonomic 2021 dataset.

Provenance

¹Contributed to nemar in BIDS format.

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

Related & sibling datasets

NM000343EEG · 15 subjSame authors NM000346EEG · 12 subj NM000163EEG · 12 subj NM000344EEG · 12 subj NM000342EEG · 12 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-SA-4.0 · DOI not on file

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000206`
Title	Neuroergonomic 2021 dataset
Author (year)	`Hinss2021_Neuroergonomic`
Canonical	—
Importable as	`NM000206`, `Hinss2021_Neuroergonomic`
Year	2023
Authors	Marcel F. Hinss, Emilie S. Jahanpour, Bertille Somon, Lou Pluchon, Frédéric Dehais, Raphaëlle N. Roy
License	CC-BY-SA-4.0
Citation / DOI	Unknown
Source links	OpenNeuro \| NeMAR \| Source URL

NM000206: eeg dataset, 15 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#