DS002691: eeg dataset, 20 subjects#

Internal attention study

Access recordings and metadata through EEGDash.

Citation: Arnaud Delorme, Dean Radin (2020). Internal attention study. 10.18112/openneuro.ds002691.v1.1.0

Modality: eeg Subjects: 20 Recordings: 20 License: CC0 Source: openneuro Citations: 2.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS002691

dataset = DS002691(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 = DS002691(cache_dir="./data", subject="01")

Advanced query

dataset = DS002691(
    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{ds002691,
  title = {Internal attention study},
  author = {Arnaud Delorme and Dean Radin},
  doi = {10.18112/openneuro.ds002691.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds002691.v1.1.0},
}

About This Dataset#

This experiment has 20 subjects. Subjects asked to mentally concentrate on a target (see published article for more information) for periods of about 15 seconds. There are 4 verbal instructions given to subject by an automated computer program connected to a speakerphone: - The instruction is to wait until the experiment starts - The instruction is to relax - The instruction is to get ready as the trial is about to start - The instruction is to mentally concentrate on the target

All the experiment is performed eye’s closed. Relax periods last for about 9 seconds, are then followed by a period of 6 seconds where the participants is asked to “get ready” for the trial, followed by a period of 15 seconds of concentration. This sequence is repeated 20 times for each participant.

Dataset Information#

Dataset ID	`DS002691`
Title	Internal attention study
Author (year)	`Delorme2020_Internal_attention`
Canonical	—
Importable as	`DS002691`, `Delorme2020_Internal_attention`
Year	2020
Authors	Arnaud Delorme, Dean Radin
License	CC0
Citation / DOI	10.18112/openneuro.ds002691.v1.1.0
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: 32
Sampling rate (Hz): 250.0
Duration (hours): 6.721111111111111

Electrode Layout#

Electrode layout — EEG · 32 sensors — 32 channels

Dataset Statistics#

Age distribution (n=20, range 23–66 yr)

2035404550556065

Sex distribution

15

5

Female Male Total: 20

Channel counts: 32 ch (n=20 recordings)

Sampling frequencies: 250.0 Hz (n=20 recordings)

Total recording duration: 6 h 43 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

Signal Preview#

Live trace viewer — sub-019 · task-internalattention

Showing one representative recording out of 20 subjects and 20 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.

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 DS002691 class to access this dataset programmatically.

class eegdash.dataset.DS002691(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Internal attention study

Study:: ds002691 (OpenNeuro)
Author (year):: Delorme2020_Internal_attention
Canonical:: —

Also importable as: DS002691, Delorme2020_Internal_attention.

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/ds002691 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002691 DOI: https://doi.org/10.18112/openneuro.ds002691.v1.1.0 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS002691
>>> dataset = DS002691(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)