EEGdash›NeMAR›ON005628

Iss. 5628 · 102 subjects · 306 recordings · CC0

Dataset Brief · Dataset of Visual and Audiovisual Stimuli in Virtual Reality…

ON005628: eeg dataset, 102 subjects#

Dataset of Visual and Audiovisual Stimuli in Virtual Reality from the Edzna Archaeological Site

Citation: Juan Pablo Rosado-Aíza, Fernando José Domínguez-Morales, Tania Yareni Pech-Canul, Paola Guadalupe Vázquez-Rodríguez, Gustavo Navas-Reascos, Luz María Alonso-Valerdi, David I. Ibarra Zarate (20). Dataset of Visual and Audiovisual Stimuli in Virtual Reality from the Edzna Archaeological Site. 10.82901/nemar.on005628

102-participant EEG dataset — Dataset of Visual and Audiovisual Stimuli in Virtual Reality from the Edzna Archaeological Site.

Data & curation Juan Pablo Rosado-Aíza · Fernando José Domínguez-Morales · Tania Yareni Pech-Canul · Paola Guadalupe Vázquez-Rodríguez · Gustavo Navas-Reascos · Luz María Alonso-Valerdi · …
Year 20 · Distributed via NeMAR

EEG · 8 ch250 HzBIDS 1.8.0Task · Edzna

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 ON005628

dataset = ON005628(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 = ON005628(cache_dir="./data", subject="01")

Advanced query

dataset = ON005628(
    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{on005628,
  title = {Dataset of Visual and Audiovisual Stimuli in Virtual Reality from the Edzna Archaeological Site},
  author = {Juan Pablo Rosado-Aíza and Fernando José Domínguez-Morales and Tania Yareni Pech-Canul and Paola Guadalupe Vázquez-Rodríguez and Gustavo Navas-Reascos and Luz María Alonso-Valerdi and David I. Ibarra Zarate},
  doi = {10.82901/nemar.on005628},
  url = {https://doi.org/10.82901/nemar.on005628},
}

§ 02Study · The README

About This Dataset#

Juan Pablo Rosado-Aíza, Fernando José Domínguez-Morales, Tania Yareni Pech-Canul, Paola Guadalupe Vázquez-Rodríguez, Gustavo Navas-Reascos, Luz María Alonso-Valerdi, David I. Ibarra Zarate

Contact person

Gustavo Navas-Reascos

https://orcid.org/0000-0003-0250-765X A01681952@tec.mx

DOI

README

Authors

Overview

Project name

View full README

DOI

README

Authors

Overview

Project name

Dataset of Visual and Audiovisual Stimuli in Virtual Reality from the Edzna Archaeological Site - Year that the project ran

2024 - Brief overview

The purpose of this dataset is to analyze user experience in a virtual reality (VR) environment, focusing on a comparative study between visual and audiovisual stimuli based on the archaeological site of Edzna, Mexico. The immersive experience allowed participants to explore the site without needing to physically being there, and the experiment was conducted in a museum setting, offering a unique experience that goes beyond traditional visual-only exhibits. The dataset includes both Electroencephalography (EEG) recordings from eight channels (Fz, C3, Cz, C4, Pz, PO7, Oz, and PO8) and user responses to the User Experience Questionnaire (UEQ), providing necessary data for future studies on how immersive environments affect user perception.

The EEG data was collected using a Unicorn Hybrid Black EEG system with a sampling rate of 250 Hz. Participants were exposed to two conditions: a visual-only stimulus and an audiovisual stimulus, both of which represented scenes from the archaeological site in VR. Prior to exposure, a baseline measurement was taken to capture the initial state of the participants. Data collection was conducted in MOSTLA, a digital innovation lab at Tecnologico de Monterrey campus, and the Museum of Contemporary Art in Monterrey. Each EEG recording is shared in .set format and follows the BIDS structure. The recordings include eight channels of brainwave recordings for the baseline, visual, and audiovisual conditions. The signals are presented in both formats: raw and preprocessed. Additionally, an .xlsx file is provided with basic participant metadata, such as age, gender, unique identifier as well as the UEQ responses.

Each EEG file contains data segmented into the three phases of the experiment: baseline, visual stimulus, and audiovisual stimulus, allowing researchers to directly compare neural responses across conditions. This dataset offers a comprehensive resource for researchers interested in investigating the effects of immersive VR environments on user engagement, and attention, making it highly applicable and useful. - Description of the contents of the dataset

sub-N - Raw data sub-Np - Preprocesed data Example: sub-1 - Raw data of subject 1

sub-1p - Preprocesed data of subject 1

Subjects

A total of 51 participants were obtained.

Apparatus

Unicorn Hybrid Black EEG system VR Headset Headphones

Experimental location

MOSTLA place at Tecnologico de Monterrey. It is located at Av. Eugenio Garza Sada 2501 Sur, Tecnologico, 64849 Monterrey, N.L., Mexico. MARCO a contemporary art museum located in Monterrey at Zuazua y Jardón, Centro, 64000 Monterrey, N.L., Mexico.

Notes

All the metadata information, including the UEQ answers could be obtained from the file metadata.xlsx The videos presented to the participants are shown in:

Audiovisual video: https://youtu.be/FBWbtSFwVuo Visual video: https://youtu.be/aLzzl0ygBnc

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 8 ch (n=306 recordings)

Sampling frequencies: 250.0 Hz (n=306 recordings)

Total recording duration: 20 h 57 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 8 ch · EEG · 250 Hz · 102 subjects, 306 recordings

Live trace viewer — sub-1 · task-Edzna · run-1

Showing one representative recording out of 102 subjects and 306 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.

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

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.ON005628(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asON005628

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

class eegdash.dataset.ON005628(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Dataset of Visual and Audiovisual Stimuli in Virtual Reality from the Edzna Archaeological Site

Study:: on005628 (NeMAR)
Author (year):: nan
Canonical:: —

Also importable as: ON005628, nan.

Modality: eeg. Subjects: 102; recordings: 306; 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/on005628 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on005628 DOI: https://doi.org/10.82901/nemar.on005628

Examples

>>> from eegdash.dataset import ON005628
>>> dataset = ON005628(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 — ON005628.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 on005628 to reproduce the tutorial on this dataset.

Citation

Juan Pablo Rosado-Aíza, Fernando José Domínguez-Morales, Tania Yareni Pech-Canul, Paola Guadalupe Vázquez-Rodríguez, Gustavo Navas-Reascos, … (20). Dataset of Visual and Audiovisual Stimuli in Virtual Reality from the Edzna Archaeological Site. 10.82901/nemar.on005628

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.on005628.

Related & sibling datasets

NM000114EEG · 64 subj ON007823EEG · 169 subj NM000255EEG · 30 subj NM000156EEG · 37 subj NM000112EEG · 123 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.8.0

Sidecars

channels · eeg.json

Provenance

CC0 · 10.82901/nemar.on005628

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`ON005628`
Title	Dataset of Visual and Audiovisual Stimuli in Virtual Reality from the Edzna Archaeological Site
Author (year)	—
Canonical	—
Importable as	`ON005628`
Year	20
Authors	Juan Pablo Rosado-Aíza, Fernando José Domínguez-Morales, Tania Yareni Pech-Canul, Paola Guadalupe Vázquez-Rodríguez, Gustavo Navas-Reascos, Luz María Alonso-Valerdi, David I. Ibarra Zarate
License	CC0
Citation / DOI	10.82901/nemar.on005628
Source links	OpenNeuro \| NeMAR \| Source URL

ON005628: eeg dataset, 102 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#