DS005868#

Flankers-FAR

Access recordings and metadata through EEGDash.

Citation: Brennan Terhune-Cotter, Phillip J. Holcomb, Katherine J. Midgley, Sofia E. Ortega, Emily M. Akers, Karen Emmorey (2025). Flankers-FAR. 10.18112/openneuro.ds005868.v1.0.1

Modality: eeg Subjects: 48 Recordings: 246 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005868

dataset = DS005868(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 = DS005868(cache_dir="./data", subject="01")

Advanced query

dataset = DS005868(
    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{ds005868,
  title = {Flankers-FAR},
  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.ds005868.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005868.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 3.28 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.

Dataset Information#

Dataset ID	`DS005868`
Title	Flankers-FAR
Year	2025
Authors	Brennan Terhune-Cotter, Phillip J. Holcomb, Katherine J. Midgley, Sofia E. Ortega, Emily M. Akers, Karen Emmorey
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005868.v1.0.1
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: 48
Recordings: 246
Tasks: 1

Channels & sampling rate

Channels: 32
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS005868 class to access this dataset programmatically.

class eegdash.dataset.DS005868(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005868. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 48; recordings: 48; 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/ds005868 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005868

Examples

>>> from eegdash.dataset import DS005868
>>> dataset = DS005868(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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None