DS007554: eeg, fnirs dataset, 30 subjects#

Multimodal dataset from the CMx7-MM Experiment

Access recordings and metadata through EEGDash.

Citation: Zaineb Ajra, Grégoire Vergotte, Stéphane Perrey, Lilian Evra, Simon Pla, Gérard Dray, Jacky Montmain, Binbin Xu (2026). Multimodal dataset from the CMx7-MM Experiment. 10.18112/openneuro.ds007554.v1.0.0

Modality: eeg, fnirs Subjects: 30 Recordings: 1034 License: CC0 Source: openneuro

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007554

dataset = DS007554(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 = DS007554(cache_dir="./data", subject="01")

Advanced query

dataset = DS007554(
    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{ds007554,
  title = {Multimodal dataset from the CMx7-MM Experiment},
  author = {Zaineb Ajra and Grégoire Vergotte and Stéphane Perrey and Lilian Evra and Simon Pla and Gérard Dray and Jacky Montmain and Binbin Xu},
  doi = {10.18112/openneuro.ds007554.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007554.v1.0.0},
}

About This Dataset#

No README content is available for this dataset.

Dataset Information#

Dataset ID	`DS007554`
Title	Multimodal dataset from the CMx7-MM Experiment
Author (year)	`Ajra2026`
Canonical	—
Importable as	`DS007554`, `Ajra2026`
Year	2026
Authors	Zaineb Ajra, Grégoire Vergotte, Stéphane Perrey, Lilian Evra, Simon Pla, Gérard Dray, Jacky Montmain, Binbin Xu
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007554.v1.0.0
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: 30
Recordings: 1034
Tasks: 7

Channels & sampling rate

Channels: 32
Sampling rate (Hz): 10.0 (519), 250.002243692634 (26), 250.00227279699027 (13), 250.00027916425347 (12), 250.00220003611236 (12), 250.0003082681523 (10), 250.0003344161922 (7), 249.99972619146294 (7), 250.0004683397171 (7), 250.00098976743945 (7), 250.00020640453602 (7), 250.00004633330667 (7), 250.0003228201043 (7), 250.00197788831903 (7), 250.0009635339967 (7), 250.00038784916032 (7), 250.00092087268928 (7), 250.00206906663882 (7), 249.9997989509009 (7), 250.0022582448113 (7), 249.99995902181342 (7), 249.99990081418517 (7), 249.99991536608968 (7), 250.00079612894984 (7), 250.0006918490773 (7), 250.00028848659534 (7), 250.0001184960745 (7), 250.0007861528667 (7), 249.99969708769964 (7), 250.0001952063645 (7), 250.0003892134066 (7), 250.00208361879578 (7), 250.00085794657687 (7), 250.00119525819466 (7), 250.0009023984434 (7), 250.00217093177304 (7), 250.00195992551573 (6), 250.0020981709544 (6), 250.0004374167852 (6), 250.0002209564761 (6), 250.00080547975216 (6), 250.00055565156606 (6), 250.00001813896364 (6), 250.00067934345654 (6), 250.00157429431061 (6), 250.0001383628698 (6), 250.00278212432153 (6), 250.00064410035029 (6), 250.00094787351506 (6), 250.00205451448358 (6), 250.0009258749471 (6), 250.00228734917093 (6), 250.00231645353733 (6), 250.00023550841792 (6), 250.00218548394184 (6), 250.0006891205781 (6), 250.0004249680335 (6), 249.99996084080226 (6), 250.00026461230658 (6), 250.00057674056998 (6), 250.00056292755625 (6), 250.00074721488596 (6), 250.0006486478473 (6), 250.00077165770458 (6), 250.00041013185162 (5), 250.0008785240374 (5), 250.00043923578096 (5), 250.00042468381545 (5), 250.00072163519718 (5), 250.00045378774817 (5), 250.00310227399197 (5), 250.00224414738952 (4), 249.99993719394965 (4), 250.0004295723662 (3), 250.0005021616826 (3), 249.99994446990382 (3), 249.99984988253266 (3), 250.00074846544865 (3), 250.00041146767637 (2), 250.00218593869715 (2), 250.00059930751343 (2)
Duration (hours): 61.88401802488944

Electrode Layout#

Electrode layout — EEG · 32 sensors — 32 channels

Dataset Statistics#

Age distribution (n=19, range 21–40 yr)

2025303540

Sex distribution

18

12

Female Male Total: 30

Channel counts: 32 ch (n=1034 recordings)

Sampling frequencies (Hz)

10250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0250.0

Total recording duration: 61 h

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 DS007554 class to access this dataset programmatically.

class eegdash.dataset.DS007554(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Multimodal dataset from the CMx7-MM Experiment

Study:: ds007554 (OpenNeuro)
Author (year):: Ajra2026
Canonical:: —

Also importable as: DS007554, Ajra2026.

Modality: eeg, fnirs; Experiment type: Other; Subject type: Healthy. Subjects: 30; recordings: 1034; tasks: 7.

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/ds007554 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007554 DOI: https://doi.org/10.18112/openneuro.ds007554.v1.0.0

Examples

>>> from eegdash.dataset import DS007554
>>> dataset = DS007554(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)