DS004771#

EEG/ERP data from a Python Reading Task

Access recordings and metadata through EEGDash.

Citation: Chu-Hsuan Kuo, Chantel S. Prat (2023). EEG/ERP data from a Python Reading Task. 10.18112/openneuro.ds004771.v1.0.0

Modality: eeg Subjects: 61 Recordings: 327 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004771

dataset = DS004771(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 = DS004771(cache_dir="./data", subject="01")

Advanced query

dataset = DS004771(
    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{ds004771,
  title = {EEG/ERP data from a Python Reading Task},
  author = {Chu-Hsuan Kuo and Chantel S. Prat},
  doi = {10.18112/openneuro.ds004771.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004771.v1.0.0},
}

About This Dataset#

EEG data for the Python reading task (acceptability judgments) described in [Kuo, C-H. and Prat, C.S. Programmers show distinct, language-like brain responses to violations in form and meaning when reading code], pending submission to Nature Communications.

This study recruited 62 total subjects. 1 subject did not complete the EEG session and was removed from all analyses and is not included in this dataset. The remaining 61 individuals’ EEG data are included. The participants info file contains information regarding which individuals were included in the final analyses (per artifact rejection criteria detailed in the article).

The stimuli for this study was administered in Presentation; as such, the files are in the formats compatible with this program.

The provided code was used for processing the EEG data. All statistics were run in Jamovi, an R-based open source software; feel free to reach out for the original files if you are interested.

Dataset Information#

Dataset ID	`DS004771`
Title	EEG/ERP data from a Python Reading Task
Year	2023
Authors	Chu-Hsuan Kuo, Chantel S. Prat
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004771.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: 61
Recordings: 327
Tasks: 1

Channels & sampling rate

Channels: 34
Sampling rate (Hz): 256.0
Duration (hours): 0.0

API Reference#

Use the DS004771 class to access this dataset programmatically.

class eegdash.dataset.DS004771(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004771. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 61; recordings: 61; 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/ds004771 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004771

Examples

>>> from eegdash.dataset import DS004771
>>> dataset = DS004771(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