EEGdash›NeMAR›NM000239

Iss. 239 · 16 subjects · 640 recordings · CC-BY-NC-SA-4.0

Dataset Brief · P-ary m-sequence-based c-VEP dataset from Martínez-Cagigal et…

NM000239: eeg dataset, 16 subjects#

P-ary m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2023)

Citation: Víctor Martínez-Cagigal, Eduardo Santamaría-Vázquez, Sergio Pérez-Velasco, Diego Marcos-Martínez, Selene Moreno-Calderón, Roberto Hornero (2023). P-ary m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2023).

16-participant EEG dataset — P-ary m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2023).

Data & curation Víctor Martínez-Cagigal · Eduardo Santamaría-Vázquez · Sergio Pérez-Velasco · Diego Marcos-Martínez · Selene Moreno-Calderón · Roberto Hornero
Year 2023 · Distributed via NeMAR
Funding Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación and ERDF (TED2021-129915B-I00, RTC2019-007350-1, PID2020-115468RB-I00) · CIBER-BBN through Instituto de Salud Carlos III

EEG · 16 ch256 Hz · mixedBIDS 1.9.0Task · cvep5 sessionsHealthyVisualPerception

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 NM000239

dataset = NM000239(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 = NM000239(cache_dir="./data", subject="01")

Advanced query

dataset = NM000239(
    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{nm000239,
  title = {P-ary m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2023)},
  author = {Víctor Martínez-Cagigal and Eduardo Santamaría-Vázquez and Sergio Pérez-Velasco and Diego Marcos-Martínez and Selene Moreno-Calderón and Roberto Hornero},
}

§ 02Study · The README

About This Dataset#

P-ary m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2023)

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

P-ary m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2023)

0.0

├─ Sensory-event
├─ Experimental-stimulus

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 16 ch (n=640 recordings)

Sampling frequencies (Hz)

256600

Total recording duration: 15 h 5 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 16 ch · EEG · 256 Hz · mixed · 16 subjects, 640 recordings

Live trace viewer — sub-2 · ses-0base2 · task-cvep · run-6

Showing one representative recording out of 16 subjects and 640 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 · 16 sensors — 16 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.NM000239(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)MartinezCagigal2023

Canonical—

Importable asNM000239 · MartinezCagigal2023

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

class eegdash.dataset.NM000239(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

P-ary m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2023)

Study:: nm000239 (NeMAR)
Author (year):: MartinezCagigal2023
Canonical:: —

Also importable as: NM000239, MartinezCagigal2023.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 16; recordings: 640; 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/nm000239 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000239

Examples

>>> from eegdash.dataset import NM000239
>>> dataset = NM000239(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 — NM000239.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 nm000239 to reproduce the tutorial on this dataset.

Citation

Víctor Martínez-Cagigal, Eduardo Santamaría-Vázquez, Sergio Pérez-Velasco, Diego Marcos-Martínez, Selene Moreno-Calderón, … (2023). P-ary m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2023).

Provenance

¹Contributed to nemar in BIDS format.

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

Related & sibling datasets

NM000240EEG · 16 subj NM000313EEG · 24 subj NM000176EEG · 5 subj NM000121EEG · 11 subj NM000123EEG · 12 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.9.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC-BY-NC-SA-4.0 · DOI not on file

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`NM000239`
Title	P-ary m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2023)
Author (year)	`MartinezCagigal2023`
Canonical	—
Importable as	`NM000239`, `MartinezCagigal2023`
Year	2023
Authors	Víctor Martínez-Cagigal, Eduardo Santamaría-Vázquez, Sergio Pérez-Velasco, Diego Marcos-Martínez, Selene Moreno-Calderón, Roberto Hornero
License	CC-BY-NC-SA-4.0
Citation / DOI	Unknown
Source links	OpenNeuro \| NeMAR \| Source URL

NM000239: eeg dataset, 16 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#