DS008711: eeg dataset, 64 subjects#
RSVP with flankers - sentences with semantic and syntactic violations
Access recordings and metadata through EEGDash.
Citation: Emily M. Akers, Katherine J. Midgley, Phillip J. Holcomb, Karen Emmorey (2026). RSVP with flankers - sentences with semantic and syntactic violations. 10.18112/openneuro.ds008711.v1.0.0
Modality: eeg Subjects: 64 Recordings: 64 License: CC0 Source: openneuro
Metadata: Complete (100%)
64-participant EEG dataset — RSVP with flankers - sentences with semantic and syntactic violations.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS008711
dataset = DS008711(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS008711(cache_dir="./data", subject="01")
Advanced query
dataset = DS008711(
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{ds008711,
title = {RSVP with flankers - sentences with semantic and syntactic violations},
author = {Emily M. Akers and Katherine J. Midgley and Phillip J. Holcomb and Karen Emmorey},
doi = {10.18112/openneuro.ds008711.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds008711.v1.0.0},
}
About This Dataset#
Data collection took place at the NeuroCognition Laboratory (NCL) in San Diego, California under the supervision of Dr. Phillip Holcomb. 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 keyboard for button pressing and wore a lightweight headset to record their verbal responses. They were instructed to watch the LCD video monitor that was at a viewing distance of 60 in (152 cm).
Participants were presented with 180 sentences, the critical center word was presented in white New Courier font on a black background, while the flanker words on each side of the critical word were presented in a grey New Courier font. Words ranged from 1 - 14 letters long and sentences ranged from 6 – 12 words long. Words were presented with a height of 80 and width of 40, the fovea visual angle ranged from .75 – 6.21 degrees, with the largest peripheral visual angle of 15.18 degrees. Conditions consisted of 30 subject-verb agreement violations, 30 semantic violations, 30 double (subject-verb agreement + semantic) violations, 30 word-order violations, and 60 control (correct) sentences. Sentences were presented in an RSVP with flankers design, with three words presented at a time in the middle of the screen, each word appeared in the middle of the screen for a duration of 400ms before appearing to slide to the right and be replaced by the next word in the sentence. There was no ISI for this task to eliminate the words from appearing to flash at the participant instead of sliding.
Cohort#
Dataset Statistics#
Age distribution (n=64, range 20–53 yr, mean 33.4 yr · sex per subject not reported)
Sex composition
Channel counts: 32 ch (n=64 recordings)
Sampling frequencies: 500.0 Hz (n=64 recordings)
Total recording duration: 27 h
Signal · Electrodes & live trace#
Live trace viewer — sub-17 · task-unnamed
Showing one representative recording out of
64 subjects and 64 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 |
RSVP with flankers - sentences with semantic and syntactic violations |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
2026 |
Authors |
Emily M. Akers, Katherine J. Midgley, Phillip J. Holcomb, Karen Emmorey |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds008711,
title = {RSVP with flankers - sentences with semantic and syntactic violations},
author = {Emily M. Akers and Katherine J. Midgley and Phillip J. Holcomb and Karen Emmorey},
doi = {10.18112/openneuro.ds008711.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds008711.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.DS008711(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
RSVP with flankers - sentences with semantic and syntactic violations
- Study:
ds008711(OpenNeuro)- Author (year):
—
- Canonical:
—
Also importable as:
DS008711.Modality:
eeg; Subject type:Unknown. Subjects: 64; recordings: 64; 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/ds008711 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds008711 DOI: https://doi.org/10.18112/openneuro.ds008711.v1.0.0
Examples
>>> from eegdash.dataset import DS008711 >>> dataset = DS008711(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.pytorch
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 ds008711 to reproduce the tutorial on this dataset.
Citation
Emily M. Akers, Katherine J. Midgley, Phillip J. Holcomb, Karen Emmorey (2026). RSVP with flankers - sentences with semantic and syntactic violations. 10.18112/openneuro.ds008711.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds008711.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset