EEGdash›NeMAR›NM000343
Iss. 343 · 15 subjects · 30 recordings · CC-BY-SA-4.0
Dataset Brief · Hinss et al. 2021 — Open multi-session and multi-task EEG cog…

NM000343: eeg dataset, 15 subjects#

Hinss et al. 2021 — Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications

Citation: Marcel F. Hinss, Emilie S. Jahanpour, Bertille Somon, Lou Pluchon, Frédéric Dehais, Raphaëlle N. Roy (2023). Hinss et al. 2021 — Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications. 10.1038/s41597-022-01898-y

15-participant EEG dataset — Hinss et al. 2021 — Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications.

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#

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000343

dataset = NM000343(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = NM000343(cache_dir="./data", subject="01")

Advanced query

dataset = NM000343(
    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{nm000343,
  title = {Hinss et al. 2021 — Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications},
  author = {Marcel F. Hinss and Emilie S. Jahanpour and Bertille Somon and Lou Pluchon and Frédéric Dehais and Raphaëlle N. Roy},
  doi = {10.1038/s41597-022-01898-y},
  url = {https://doi.org/10.1038/s41597-022-01898-y},
}
§ 02Study · The README

About This Dataset#

Neuroergonomic 2021 dataset.

Code: Hinss2021

Paradigm: rstate DOI: 10.1038/s41597-022-01898-y Subjects: 15 Sessions per subject: 2 Events: rs=1, easy=2, medium=3, diff=4 Trial interval: [0, 2] s File format: set

Hinss2021

Acquisition

Sampling rate: 500.0 Hz Number of channels: 62 Channel types: eeg=62 Channel names: AF3, AF4, AF7, AF8, AFz, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, F1, F2, F3, F4, F5, F6, F7, F8, FC1, FC2, FC3, FC4, FC5, FC6, FCz, FT10, FT7, FT8, FT9, Fp1, Fp2, Fz, O1, O2, Oz, P1, P2, P3, P4, P5, P6, P7, P8, PO3, PO4, PO7, PO8, POz, Pz, T7, T8, TP7, TP8 Montage: standard_1020 Hardware: ActiCHamp (Brain Products Gmbh)

View full README

Hinss2021

Acquisition

Sampling rate: 500.0 Hz Number of channels: 62 Channel types: eeg=62 Channel names: AF3, AF4, AF7, AF8, AFz, C1, C2, C3, C4, C5, C6, CP1, CP2, CP3, CP4, CP5, CP6, CPz, F1, F2, F3, F4, F5, F6, F7, F8, FC1, FC2, FC3, FC4, FC5, FC6, FCz, FT10, FT7, FT8, FT9, Fp1, Fp2, Fz, O1, O2, Oz, P1, P2, P3, P4, P5, P6, P7, P8, PO3, PO4, PO7, PO8, POz, Pz, T7, T8, TP7, TP8 Montage: standard_1020 Hardware: ActiCHamp (Brain Products Gmbh) Reference: Fpz Sensor type: active Ag/AgCl Line frequency: 50.0 Hz Impedance threshold: 25 kOhm Auxiliary channels: ecg

Participants

Number of subjects: 15 Health status: healthy Age: mean=23.9 Gender distribution: female=11, male=18

Experimental Protocol

Paradigm: rstate Number of classes: 4 Class labels: rs, easy, medium, diff Study design: Passive BCI neuroergonomics dataset with resting state and 3 difficulty levels of MATB-II task (easy, medium, difficult). The MOABB loader provides resting state and MATB conditions only. Feedback type: none Stimulus type: visual display Training/test split: False

HED Event Annotations

Schema: HED 8.4.0 | Browse: https://www.hedtags.org/hed-schema-browser 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-1 · ses-1 · 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.

NEMAR pipeline statistics — NM000343
HED event descriptors word cloud HED event descriptors word cloud — NM000343
§ 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

NM000343

Title

Hinss et al. 2021 — Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications

Author (year)

Hinss2021

Canonical

—

Importable as

NM000343, Hinss2021

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

doi:10.1038/s41597-022-01898-y

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{nm000343,
  title = {Hinss et al. 2021 — Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications},
  author = {Marcel F. Hinss and Emilie S. Jahanpour and Bertille Somon and Lou Pluchon and Frédéric Dehais and Raphaëlle N. Roy},
  doi = {10.1038/s41597-022-01898-y},
  url = {https://doi.org/10.1038/s41597-022-01898-y},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.NM000343(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Hinss2021
Canonical—
Importable asNM000343 · Hinss2021
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.NM000343(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Hinss et al. 2021 — Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications

Study:

nm000343 (NeMAR)

Author (year):

Hinss2021

Canonical:

—

Also importable as: NM000343, Hinss2021.

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/nm000343 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000343 DOI: https://doi.org/10.1038/s41597-022-01898-y

Examples

>>> from eegdash.dataset import NM000343
>>> dataset = NM000343(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 — NM000343.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 nm000343 to reproduce the tutorial on this dataset.

Citation

Marcel F. Hinss, Emilie S. Jahanpour, Bertille Somon, Lou Pluchon, Frédéric Dehais, … (2023). Hinss et al. 2021 — Open multi-session and multi-task EEG cognitive Dataset for passive brain-computer Interface Applications. 10.1038/s41597-022-01898-y

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.1038/s41597-022-01898-y.

BIDS
BIDS 1.9.0
Sidecars
events · events.json · channels · eeg.json
Provenance
CC-BY-SA-4.0 · 10.1038/s41597-022-01898-y
Machine-readable
schema.org/Dataset · Croissant
Mirrors
OpenNeuro · NEMAR · HF org

See Also#