DS004817: eeg dataset, 20 subjects#

EEG-attention-rsvp-exp2

Access recordings and metadata through EEGDash.

Citation: Grootswagers, Tijl, Robinson, Amanda, Shatek, Sofia, Carlson, Thomas (2023). EEG-attention-rsvp-exp2. 10.18112/openneuro.ds004817.v1.0.0

Modality: eeg Subjects: 20 Recordings: 20 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004817

dataset = DS004817(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 = DS004817(cache_dir="./data", subject="01")

Advanced query

dataset = DS004817(
    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{ds004817,
  title = {EEG-attention-rsvp-exp2},
  author = {Grootswagers, Tijl and Robinson, Amanda and Shatek, Sofia and Carlson, Thomas},
  doi = {10.18112/openneuro.ds004817.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004817.v1.0.0},
}

About This Dataset#

EEG data for Grootswagers et al 2021 experiment 2 (small objects on big letters) Grootswagers T., Robinson A.K., Shatek S.M., Carlson T.A. (2021). The neural dynamics underlying prioritisation of task-relevant information. Neurons, Behaviour, Data Analysis, and Theory, 5(1) https://doi.org/10.51628/001c.19129 See also https://osf.io/7zhwp/ and https://openneuro.org/datasets/ds004816

Dataset Information#

Dataset ID	`DS004817`
Title	EEG-attention-rsvp-exp2
Author (year)	`Grootswagers2023_E2`
Canonical	—
Importable as	`DS004817`, `Grootswagers2023_E2`
Year	2023
Authors	Grootswagers, Tijl, Robinson, Amanda, Shatek, Sofia, Carlson, Thomas
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004817.v1.0.0
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: 20
Recordings: 20
Tasks: 1

Channels & sampling rate

Channels: 63
Sampling rate (Hz): 1000.0
Duration (hours): Not calculated

Electrode Layout#

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

Dataset Statistics#

Age distribution (n=20, range 19–36 yr)

15202535

Sex distribution

17

3

Female Male Total: 20

Channel counts: 63 ch (n=20 recordings)

Sampling frequencies: 1000.0 Hz (n=20 recordings)

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 DS004817 class to access this dataset programmatically.

class eegdash.dataset.DS004817(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

EEG-attention-rsvp-exp2

Study:: ds004817 (OpenNeuro)
Author (year):: Grootswagers2023_E2
Canonical:: —

Also importable as: DS004817, Grootswagers2023_E2.

Modality: eeg. Subjects: 20; recordings: 20; 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/ds004817 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004817 DOI: https://doi.org/10.18112/openneuro.ds004817.v1.0.0 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS004817
>>> dataset = DS004817(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)