NM000272: eeg dataset, 22 subjects#

Romani et al. 2025 — BrainForm: a Serious Game for BCI Training and Data Collection (P300 ERP, University of Trento)

Access recordings and metadata through EEGDash.

Citation: Michele Romani, Devis Zanoni, Elisabetta Farella, Luca Turchet (—). Romani et al. 2025 — BrainForm: a Serious Game for BCI Training and Data Collection (P300 ERP, University of Trento). 10.48550/arXiv.2510.10169

Modality: eeg Subjects: 22 Recordings: 120 License: CC-BY-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000272

dataset = NM000272(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 = NM000272(cache_dir="./data", subject="01")

Advanced query

dataset = NM000272(
    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{nm000272,
  title = {Romani et al. 2025 — BrainForm: a Serious Game for BCI Training and Data Collection (P300 ERP, University of Trento)},
  author = {Michele Romani and Devis Zanoni and Elisabetta Farella and Luca Turchet},
  doi = {10.48550/arXiv.2510.10169},
  url = {https://doi.org/10.48550/arXiv.2510.10169},
}

About This Dataset#

No README content is available for this dataset.

Dataset Information#

Dataset ID	`NM000272`
Title	Romani et al. 2025 — BrainForm: a Serious Game for BCI Training and Data Collection (P300 ERP, University of Trento)
Author (year)	`Romani2025_BF_ERP`
Canonical	—
Importable as	`NM000272`, `Romani2025_BF_ERP`
Year	—
Authors	Michele Romani, Devis Zanoni, Elisabetta Farella, Luca Turchet
License	CC-BY-4.0
Citation / DOI	doi:10.48550/arXiv.2510.10169
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: 22
Recordings: 120
Tasks: 1

Channels & sampling rate

Channels: 8
Sampling rate (Hz): 250.0
Duration (hours): 6.27819111111111

Electrode Layout#

Electrode layout — EEG · 8 sensors — 8 channels

Dataset Statistics#

Channel counts: 8 ch (n=120 recordings)

Sampling frequencies: 250.0 Hz (n=120 recordings)

Total recording duration: 6 h 16 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 NM000272 class to access this dataset programmatically.

class eegdash.dataset.NM000272(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Romani et al. 2025 — BrainForm: a Serious Game for BCI Training and Data Collection (P300 ERP, University of Trento)

Study:: nm000272 (NeMAR)
Author (year):: Romani2025_BF_ERP
Canonical:: —

Also importable as: NM000272, Romani2025_BF_ERP.

Modality: eeg; Experiment type: Attention; Subject type: Unknown. Subjects: 22; recordings: 120; 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/nm000272 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000272 DOI: https://doi.org/10.48550/arXiv.2510.10169

Examples

>>> from eegdash.dataset import NM000272
>>> dataset = NM000272(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)