DS005866: eeg dataset, 60 subjects#
Flankers-NEAR
Citation: Brennan Terhune-Cotter, Phillip J. Holcomb, Katherine J. Midgley, Sofia E. Ortega, Emily M. Akers, Karen Emmorey (—). Flankers-NEAR. 10.18112/openneuro.ds005866.v1.0.1
60-participant EEG dataset — Flankers-NEAR.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS005866
dataset = DS005866(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS005866(cache_dir="./data", subject="01")
Advanced query
dataset = DS005866(
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{ds005866,
title = {Flankers-NEAR},
author = {Brennan Terhune-Cotter and Phillip J. Holcomb and Katherine J. Midgley and Sofia E. Ortega and Emily M. Akers and Karen Emmorey},
doi = {10.18112/openneuro.ds005866.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds005866.v1.0.1},
}
About This Dataset#
Data collection took place at the NeuroCognition Laboratory (NCL) in San Diego, California under the supervision of Dr. Phillip Holcomb and Dr. Karen Emmorey. This project followed the San Diego State University’s IRB guidelines.
Participants sat in a comfortable chair in a darkened, sound-attenuated room throughout the experiment. They were given a game controller for responding to stimuli. They were instructed to watch the 24in-LCD video monitor, which was placed at a viewing distance of 60 in (152 cm).
Participants were presented with 90 four-letter real words and 90 four-letter pseudowords in white New Courier font on a black background. Each letter subtended .41 degrees of visual angle. The flanker words were separated from the center target word by .41 degrees of empty space on both sides. All targets and flankers were content words under a 6th grade reading level; plural words and proper nouns were excluded. All words were presented once in each of the three conditions: no flanker, identical flankers, or different flankers. There were 270 trials. Trials started with a purple fixation cross for 1000ms, followed by a white fixation cross for 500ms to prepare participants for the presentation of the stimulus. The stimulus item was then presented for 150ms, followed by a blank screen shown until participants responded via the game controller.
Cohort#
Dataset Statistics#
Age distribution (n=60, range 20–54 yr, mean 31.9 yr · sex per subject not reported)
Sex composition
Channel counts: 32 ch (n=60 recordings)
Sampling frequencies: 500.0 Hz (n=60 recordings)
Total recording duration: 15 h 58 min
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-FlankerNEAR
Showing one representative recording out of
60 subjects and 60 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
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 |
Flankers-NEAR |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Brennan Terhune-Cotter, Phillip J. Holcomb, Katherine J. Midgley, Sofia E. Ortega, Emily M. Akers, Karen Emmorey |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds005866,
title = {Flankers-NEAR},
author = {Brennan Terhune-Cotter and Phillip J. Holcomb and Katherine J. Midgley and Sofia E. Ortega and Emily M. Akers and Karen Emmorey},
doi = {10.18112/openneuro.ds005866.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds005866.v1.0.1},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005866 · TerhuneCotter2025_NEAReegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005866(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Flankers-NEAR
- Study:
ds005866(OpenNeuro)- Author (year):
TerhuneCotter2025_NEAR- Canonical:
—
Also importable as:
DS005866,TerhuneCotter2025_NEAR.Modality:
eeg; Experiment type:Attention; Subject type:Healthy. Subjects: 60; recordings: 60; 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/ds005866 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005866 DOI: https://doi.org/10.18112/openneuro.ds005866.v1.0.1
Examples
>>> from eegdash.dataset import DS005866 >>> dataset = DS005866(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/ds005866").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 ds005866 to reproduce the tutorial on this dataset.
Citation
Brennan Terhune-Cotter, Phillip J. Holcomb, Katherine J. Midgley, Sofia E. Ortega, Emily M. Akers, … (n.d.). Flankers-NEAR. 10.18112/openneuro.ds005866.v1.0.1
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds005866.v1.0.1.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset