NM000240: eeg dataset, 16 subjects#

Checkerboard m-sequence-based c-VEP dataset from

Access recordings and metadata through EEGDash.

Citation: Álvaro Fernández-Rodríguez, Víctor Martínez-Cagigal, Eduardo Santamaría-Vázquez, Ricardo Ron-Angevin, Roberto Hornero (2025). Checkerboard m-sequence-based c-VEP dataset from.

Modality: eeg Subjects: 16 Recordings: 383 License: CC-BY-NC-SA-4.0 Source: nemar

Metadata: Complete (90%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import NM000240

dataset = NM000240(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 = NM000240(cache_dir="./data", subject="01")

Advanced query

dataset = NM000240(
    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{nm000240,
  title = {Checkerboard m-sequence-based c-VEP dataset from},
  author = {Álvaro Fernández-Rodríguez and Víctor Martínez-Cagigal and Eduardo Santamaría-Vázquez and Ricardo Ron-Angevin and Roberto Hornero},
}

About This Dataset#

Checkerboard m-sequence-based c-VEP dataset from

Checkerboard m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2025) and Fernández-Rodríguez et al. (2023).

Dataset Overview

Code: MartinezCagigal2023Checkercvep
Paradigm: cvep
DOI: https://doi.org/10.71569/7c67-v596

View full README

Checkerboard m-sequence-based c-VEP dataset from

Checkerboard m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2025) and Fernández-Rodríguez et al. (2023).

Dataset Overview

Code: MartinezCagigal2023Checkercvep
Paradigm: cvep
DOI: https://doi.org/10.71569/7c67-v596
Subjects: 16
Sessions per subject: 8
Events: 0.0=100, 1.0=101
Trial interval: (0, 1) s
Runs per session: 3

Acquisition

Sampling rate: 256.0 Hz
Number of channels: 16
Channel types: eeg=16
Montage: standard_1005
Line frequency: 50.0 Hz

Participants

Number of subjects: 16
Health status: healthy

Experimental Protocol

Paradigm: cvep
Number of classes: 2
Class labels: 0.0, 1.0

HED Event Annotations

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

0.0

     ├─ Sensory-event
     ├─ Experimental-stimulus
     ├─ Visual-presentation
     └─ Label/intensity_0_0

1.0

├─ Sensory-event
├─ Experimental-stimulus
├─ Visual-presentation
└─ Label/intensity_1_0

Documentation

DOI: 10.71569/7c67-v596
Associated paper DOI: 10.3389/fnhum.2023.1288438
License: CC-BY-NC-SA-4.0
Investigators: Álvaro Fernández-Rodríguez, Víctor Martínez-Cagigal, Eduardo Santamaría-Vázquez, Ricardo Ron-Angevin, Roberto Hornero
Senior author: Roberto Hornero
Contact: victor.martinez@gib.tel.uva.es
Institution: University of Valladolid
Department: Biomedical Engineering Group, ETSIT
Address: Paseo de Belén, 15, 47011, Valladolid, Spain
Country: ES
Repository: U Valladoid
Data URL: https://doi.org/10.71569/7c67-v596
Publication year: 2023
Ethics approval: Approved by the local ethics committee; all participants provided informed consent
How to acknowledge: Please cite: Fernández-Rodríguez et al. (2023). Influence of spatial frequency in visual stimuli for cVEP-based BCIs: evaluation of performance and user experience. Frontiers in Human Neuroscience, 17, 1288438. https://doi.org/10.3389/fnhum.2023.1288438

References

Martínez Cagigal, V. (2025). Dataset: Influence of spatial frequency in visual stimuli for cVEP-based BCIs: evaluation of performance and user experience. https://doi.org/10.71569/7c67-v596 Fernández-Rodríguez, Á., Martínez-Cagigal, V., Santamaría-Vázquez, E., Ron-Angevin, R., & Hornero, R. (2023). Influence of spatial frequency in visual stimuli for cVEP-based BCIs: evaluation of performance and user experience. Frontiers in Human Neuroscience, 17, 1288438. https://doi.org/10.3389/fnhum.2023.1288438 Santamaría-Vázquez, E., Martínez-Cagigal, V., Marcos-Martínez, D., Rodríguez-González, V., Pérez-Velasco, S., Moreno-Calderón, S., & Hornero, R. (2023). MEDUSA©: A novel Python-based software ecosystem to accelerate brain–computer interface and cognitive neuroscience research. Computer Methods and Programs in Biomedicine, 230, 107357. https://doi.org/10.1016/j.cmpb.2023.107357 Notes Although the dataset was recorded in a single session, each condition is stored as a separate session to match the MOABB structure. Within each session, three runs are available (two for training, one for testing). .. versionadded:: 1.2.0 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	`NM000240`
Title	Checkerboard m-sequence-based c-VEP dataset from
Author (year)	`FernandezRodriguez2025`
Canonical	—
Importable as	`NM000240`, `FernandezRodriguez2025`
Year	2025
Authors	Álvaro Fernández-Rodríguez, Víctor Martínez-Cagigal, Eduardo Santamaría-Vázquez, Ricardo Ron-Angevin, Roberto Hornero
License	CC-BY-NC-SA-4.0
Citation / DOI	Unknown
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: 16
Recordings: 383
Tasks: 1

Channels & sampling rate

Channels: 16
Sampling rate (Hz): 256.0
Duration (hours): 13.408473307291668

Electrode Layout#

Electrode layout — EEG · 16 sensors — 16 channels

Dataset Statistics#

Channel counts: 16 ch (n=383 recordings)

Sampling frequencies: 256.0 Hz (n=383 recordings)

Total recording duration: 13 h 24 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 NM000240 class to access this dataset programmatically.

class eegdash.dataset.NM000240(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Checkerboard m-sequence-based c-VEP dataset from

Study:: nm000240 (NeMAR)
Author (year):: FernandezRodriguez2025
Canonical:: —

Also importable as: NM000240, FernandezRodriguez2025.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 16; recordings: 383; 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/nm000240 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=nm000240

Examples

>>> from eegdash.dataset import NM000240
>>> dataset = NM000240(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)