DS005296#

Sentence semantic and syntactic violations

Access recordings and metadata through EEGDash.

Citation: Karen Emmorey, Emily M. Akers, Katherine J. Midgley, Phillip J. Holcomb (2024). Sentence semantic and syntactic violations. 10.18112/openneuro.ds005296.v1.0.0

Modality: eeg Subjects: 62 Recordings: 317 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005296

dataset = DS005296(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 = DS005296(cache_dir="./data", subject="01")

Advanced query

dataset = DS005296(
    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{ds005296,
  title = {Sentence semantic and syntactic violations},
  author = {Karen Emmorey and Emily M. Akers and Katherine J. Midgley and Phillip J. Holcomb},
  doi = {10.18112/openneuro.ds005296.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005296.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 60in.

Participants were presented with 180 sentences in white font on a black background. 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 design, one word at a time, in the middle of the screen for a duration of 600ms with an ISI of 200ms.

Dataset Information#

Dataset ID	`DS005296`
Title	Sentence semantic and syntactic violations
Year	2024
Authors	Karen Emmorey, Emily M. Akers, Katherine J. Midgley, Phillip J. Holcomb
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005296.v1.0.0
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: 62
Recordings: 317
Tasks: 1

Channels & sampling rate

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

API Reference#

Use the DS005296 class to access this dataset programmatically.

class eegdash.dataset.DS005296(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005296. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 62; recordings: 62; 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/ds005296 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005296

Examples

>>> from eegdash.dataset import DS005296
>>> dataset = DS005296(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