EEGdash›NeMAR›ON004018

Iss. 4018 · 16 subjects · 32 recordings · CC0

Dataset Brief · EEG recordings for 200 object images presented in RSVP sequen…

ON004018: eeg dataset, 16 subjects#

EEG recordings for 200 object images presented in RSVP sequences at 5Hz or 20Hz

Citation: Grootswagers, T., Robinson, A.K., Carlson, T.A. (2019). EEG recordings for 200 object images presented in RSVP sequences at 5Hz or 20Hz. 10.82901/nemar.on004018

16-participant EEG dataset — EEG recordings for 200 object images presented in RSVP sequences at 5Hz or 20Hz.

Data & curation Grootswagers, T. · Robinson, A.K. · Carlson, T.A.
Year 2019 · Distributed via NeMAR

EEG · 63 ch1000 HzBIDS 1.0.0 + bep006Task · rsvp

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 ON004018

dataset = ON004018(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 = ON004018(cache_dir="./data", subject="01")

Advanced query

dataset = ON004018(
    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{on004018,
  title = {EEG recordings for 200 object images presented in RSVP sequences at 5Hz or 20Hz},
  author = {Grootswagers, T. and Robinson, A.K. and Carlson, T.A.},
  doi = {10.82901/nemar.on004018},
  url = {https://doi.org/10.82901/nemar.on004018},
}

§ 02Study · The README

About This Dataset#

Grootswagers T.*, Robinson A.K.*, Carlson T.A. (2019). The representational dynamics of visual objects in rapid serial visual processing streams. NeuroImage, 188, 668-679 https://doi.org/10.1016/j.neuroimage.2018.12.046

Cohort#

Dataset Statistics#

Age distribution by gender (n=14, range 18–38 yr, mean 26.4 yr)

1520253035

Female · 5Male · 9

Sex composition

Female
5
Male
11
F : M ratio
0.45 : 1

31% female · n = 16 subjects with reported sex.

Channel counts: 63 ch (n=32 recordings)

Sampling frequencies: 1000.0 Hz (n=32 recordings)

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 63 ch · EEG · 1000 Hz · 16 subjects, 32 recordings

Live trace viewer — sub-01 · task-rsvp · run-01

Showing one representative recording out of 16 subjects and 32 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.ON004018(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asON004018

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

class eegdash.dataset.ON004018(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

EEG recordings for 200 object images presented in RSVP sequences at 5Hz or 20Hz

Study:: on004018 (NeMAR)
Author (year):: nan
Canonical:: —

Also importable as: ON004018, nan.

Modality: eeg. Subjects: 16; recordings: 32; 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/on004018 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on004018 DOI: https://doi.org/10.82901/nemar.on004018

Examples

>>> from eegdash.dataset import ON004018
>>> dataset = ON004018(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 — ON004018.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 on004018 to reproduce the tutorial on this dataset.

Citation

Grootswagers, T., Robinson, A.K., Carlson, T.A. (2019). EEG recordings for 200 object images presented in RSVP sequences at 5Hz or 20Hz. 10.82901/nemar.on004018

Provenance

¹Contributed to nemar in BIDS format.

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

³Persistent identifier: 10.82901/nemar.on004018.

Related & sibling datasets

ON005028EEG · 11 subj ON004745EEG · 6 subj ON005262EEG · 12 subj NM000150EEG ON003380EEG · 1 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.0.0 + bep006

Sidecars

events

Provenance

CC0 · 10.82901/nemar.on004018

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`ON004018`
Title	EEG recordings for 200 object images presented in RSVP sequences at 5Hz or 20Hz
Author (year)	—
Canonical	—
Importable as	`ON004018`
Year	2019
Authors	Grootswagers, T., Robinson, A.K., Carlson, T.A.
License	CC0
Citation / DOI	10.82901/nemar.on004018
Source links	OpenNeuro \| NeMAR \| Source URL

ON004018: 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#