EEGdash›NeMAR›NM000290

Iss. 290 · 10 subjects · 112 recordings · CC-BY-4.0

Dataset Brief · PROTEUS BCI Bordeaux — EEG/EMG Foundation Challenge 2026, Tra…

NM000290: eeg dataset, 10 subjects#

PROTEUS BCI Bordeaux — EEG/EMG Foundation Challenge 2026, Track 02

Access recordings and metadata through EEGDash.

Citation: Dreyer Pauline, Bourdil Manon, Bechon Loic, Kojima Simon, Velut Sebastien, Rimbert Sebastien, Roy Raphaëlle, Lotte Fabien (20). PROTEUS BCI Bordeaux — EEG/EMG Foundation Challenge 2026, Track 02. 10.82901/nemar.nm000290

Modality: eeg Subjects: 10 Recordings: 112 License: CC-BY-4.0 Source: nemar

Metadata: Complete (100%)

10-participant EEG dataset — PROTEUS BCI Bordeaux — EEG/EMG Foundation Challenge 2026, Track 02.

Data & curation Dreyer Pauline · Bourdil Manon · Bechon Loic · Kojima Simon · Velut Sebastien · Rimbert Sebastien · …
Senior author Lotte Fabien
Year 20 · Distributed via NeMAR
Funding PROTEUS ANR

EEG · 41 ch500 HzBIDS 1.9.06 tasks3 sessions

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 NM000290

dataset = NM000290(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 = NM000290(cache_dir="./data", subject="01")

Advanced query

dataset = NM000290(
    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{nm000290,
  title = {PROTEUS BCI Bordeaux — EEG/EMG Foundation Challenge 2026, Track 02},
  author = {Dreyer Pauline and Bourdil Manon and Bechon Loic and Kojima Simon and Velut Sebastien and Rimbert Sebastien and Roy Raphaëlle and Lotte Fabien},
  doi = {10.82901/nemar.nm000290},
  url = {https://doi.org/10.82901/nemar.nm000290},
}

§ 02Study · The README

About This Dataset#

EEG recorded with a 64-channel actiCAP slim / actiCHamp system (Brain Products); 41 EEG channels are provided,

sampled at 500 Hz, reference M1 (mastoid), ground FpZ, stored as EDF+ (µV, per-file digital scaling).

Sessions per participant: 1, 3. Session labels are chronological.

DOI

PROTEUS BCI Bordeaux — EEG/EMG Foundation Challenge 2026, Track 02

Each session contains up to 8 runs: - BaselineOE / BaselineCE (run 0): rest, eyes open / eyes closed. - AcquisitionGraz, AcquisitionBH: calibration runs with sham feedback (Graz and BrainHero interfaces). - OnlineRawGraz, OnlineRawBH (two runs each): online runs with real feedback.
Three cued mental tasks: mi (kinesthetic motor imagery of the non-dominant hand), sub (mental subtraction), word (word generation from a given letter). Cue onsets are the mi / sub / word rows of *_events.tsv (OpenViBE codes 33024 / 33025 / 33026, also stored as EDF+ annotations).
participants.tsv: age (binned). sub-*_sessions.tsv: session labels only (questionnaires withheld).

Known issues

BAD_ACQ_SKIP marks the padding (< 1 s) at the end of the last 1-s EDF record of each run; it is not a gap.
1 runs have an EDF resolution coarser than 1 µV per bit (per-file digital scaling).
13 cued runs have fewer trials than is usual for their run type.

Licence

Creative Commons Attribution 4.0 International (CC-BY-4.0). Authors: Dreyer Pauline, Bourdil Manon, Bechon Loic, Kojima Simon, Velut Sebastien, Rimbert Sebastien, Roy Raphaëlle, Lotte Fabien.

NEMAR Metadata#

[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.nm000290-blue)](https://doi.org/10.82901/nemar.nm000290) # PROTEUS BCI Bordeaux — EEG/EMG Foundation Challenge 2026, Track 02 EEG recorded with a 64-channel actiCAP slim / actiCHamp system (Brain Products); 41 EEG channels are provided, sampled at 500 Hz, reference M1 (mastoid), ground FpZ, stored as EDF+ (µV, per-file digital scaling). ## Contents - 10 participants (labels sub-P##), 14 sessions, 112 runs.

Sessions per participant: 1, 3. Session labels are chronological.

Each session contains up to 8 runs: - BaselineOE / BaselineCE (run 0): rest, eyes open / eyes closed. - AcquisitionGraz, AcquisitionBH: calibration runs with sham feedback (Graz and BrainHero interfaces). - OnlineRawGraz, OnlineRawBH (two runs each): online runs with real feedback.
Three cued mental tasks: mi (kinesthetic motor imagery of the non-dominant hand), sub (mental subtraction), word (word generation from a given letter). Cue onsets are the mi / sub / word rows of *_events.tsv (OpenViBE codes 33024 / 33025 / 33026, also stored as EDF+ annotations).
participants.tsv: age (binned). sub-*_sessions.tsv: session labels only (questionnaires withheld).

## Known issues - BAD_ACQ_SKIP marks the padding (< 1 s) at the end of the last 1-s EDF record of each run; it is not a gap. - 1 runs have an EDF resolution coarser than 1 µV per bit (per-file digital scaling). - 13 cued runs have fewer trials than is usual for their run type. ## Licence Creative Commons Attribution 4.0 International (CC-BY-4.0). Authors: Dreyer Pauline, Bourdil Manon, Bechon Loic, Kojima Simon, Velut Sebastien, Rimbert Sebastien, Roy Raphaëlle, Lotte Fabien.

License: CC-BY-4.0

Authors:

Dreyer Pauline
Bourdil Manon
Bechon Loic
Kojima Simon
Velut Sebastien
… and 3 more

Versions:

Version	DOI	Released
`current`	10.82901/nemar.nm000290

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 41 ch (n=112 recordings)

Sampling frequencies: 500.0 Hz (n=112 recordings)

Total recording duration: 10 h 45 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 41 ch · EEG · 500 Hz · 10 subjects, 112 recordings

Live trace viewer — sub-P01 · ses-1 · task-AcquisitionBH · run-2

Showing one representative recording out of 10 subjects and 112 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 · 41 sensors — 41 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.NM000290(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asNM000290

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

class eegdash.dataset.NM000290(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

PROTEUS BCI Bordeaux — EEG/EMG Foundation Challenge 2026, Track 02

Study:: nm000290 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: NM000290.

Modality: eeg; Subject type: Unknown. Subjects: 10; recordings: 0; tasks: 6.

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/nm000290 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000290 DOI: https://doi.org/10.82901/nemar.nm000290

Examples

>>> from eegdash.dataset import NM000290
>>> dataset = NM000290(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 — NM000290.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 nm000290 to reproduce the tutorial on this dataset.

Citation

Dreyer Pauline, Bourdil Manon, Bechon Loic, Kojima Simon, Velut Sebastien, … (20). PROTEUS BCI Bordeaux — EEG/EMG Foundation Challenge 2026, Track 02. 10.82901/nemar.nm000290

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.nm000290.

Related & sibling datasets

NM000133EEG · 8 subj NM000174EEG · 15 subj ON007813EEG · 4 subj NM000220EEG · 3 subj NM000177EEG · 18 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-4.0 · 10.82901/nemar.nm000290

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000290`
Title	PROTEUS BCI Bordeaux — EEG/EMG Foundation Challenge 2026, Track 02
Author (year)	—
Canonical	—
Importable as	`NM000290`
Year	20
Authors	Dreyer Pauline, Bourdil Manon, Bechon Loic, Kojima Simon, Velut Sebastien, Rimbert Sebastien, Roy Raphaëlle, Lotte Fabien
License	CC-BY-4.0
Citation / DOI	10.82901/nemar.nm000290
Source links	OpenNeuro \| NeMAR \| Source URL

NM000290: eeg dataset, 10 subjects#

Quickstart#

About This Dataset#

NEMAR Metadata#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#