NM000343: eeg dataset, 15 subjects#

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

Access recordings and metadata through EEGDash.

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

Modality: eeg Subjects: 15 Recordings: 30 License: CC-BY-SA-4.0 Source: nemar

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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},
}

About This Dataset#

Hinss2021

Neuroergonomic 2021 dataset.

Dataset Overview

Code: Hinss2021 Paradigm: rstate DOI: 10.1038/s41597-022-01898-y

View full README

Hinss2021

Neuroergonomic 2021 dataset.

Dataset Overview

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

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

Dataset Information#

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

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: 15
Recordings: 30
Tasks: 1

Channels & sampling rate

Channels: 61
Sampling rate (Hz): 500.0
Duration (hours): 3.974983333333333

Electrode Layout#

Electrode layout — EEG · 61 sensors — 61 channels

Dataset Statistics#

Age distribution (n=15, range 23–23 yr)

20

Channel counts: 61 ch (n=30 recordings)

Sampling frequencies: 500.0 Hz (n=30 recordings)

Total recording duration: 3 h 58 min

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 NM000343 class to access this dataset programmatically.

class eegdash.dataset.NM000343(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

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)