DS002691: eeg dataset, 20 subjects#
Internal attention study
Citation: Arnaud Delorme, Dean Radin (—). Internal attention study. 10.18112/openneuro.ds002691.v1.1.0
20-participant EEG dataset — Internal attention study.
Quickstart#
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)
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'])
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.
Cohort#
Dataset Statistics#
Age distribution by gender (n=20, range 23–66 yr, mean 46.9 yr)
Sex composition
Channel counts: 32 ch (n=20 recordings)
Sampling frequencies: 250.0 Hz (n=20 recordings)
Total recording duration: 6 h 43 min
Signal · Electrodes & live trace#
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.
Electrode layout — EEG · 32 sensors — 32 channels
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
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.
Full dataset metadata table
Dataset ID |
|
Title |
Internal attention study |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Arnaud Delorme, Dean Radin |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS002691 · Delorme2020_Internal_attentioneegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS002691(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand 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)
- 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.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds002691").huggingface
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 ds002691 to reproduce the tutorial on this dataset.
Citation
Arnaud Delorme, Dean Radin (n.d.). Internal attention study. 10.18112/openneuro.ds002691.v1.1.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds002691.v1.1.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset