DS004043#

The time-course of feature-based attention effects dissociated from temporal expectation and target-related processes

Access recordings and metadata through EEGDash.

Citation: Moerel, Denise, Grootswagers, Tijl, Robinson, Amanda, Shatek, Sophia, Woolgar, Alexandra, Carlson, Thomas, Rich, Anina (2022). The time-course of feature-based attention effects dissociated from temporal expectation and target-related processes. 10.18112/openneuro.ds004043.v1.1.0

Modality: eeg Subjects: 20 Recordings: 84 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004043

dataset = DS004043(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 = DS004043(cache_dir="./data", subject="01")

Advanced query

dataset = DS004043(
    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{ds004043,
  title = {The time-course of feature-based attention effects dissociated from temporal expectation and target-related processes},
  author = {Moerel, Denise and Grootswagers, Tijl and Robinson, Amanda and Shatek, Sophia and Woolgar, Alexandra and Carlson, Thomas and Rich, Anina},
  doi = {10.18112/openneuro.ds004043.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds004043.v1.1.0},
}

About This Dataset#

Experiment Details

Human electroencephalography recordings from 20 participants. Participants viewed rapid sequences of overlaid oriented grating pairs while detecting a “target” grating of a particular orientation. We manipulated attention, one grating was attended and the other ignored (cued by colour), and temporal expectation, with stimulus onset timing either predictable or not.

Experiment length: 1 hour

More information:

https://doi.org/10.17605/OSF.IO/5B8K6 (OSF repository with more information and example analysis code)

Moerel, D., Grootswagers, T., Robinson, A. K., Shatek, S. M., Woolgar, A., Carlson, T. A., & Rich, A. N. (2021). Undivided attention: The temporal effects of attention dissociated from decision, memory, and expectation. bioRxiv. doi: https://doi.org/10.1101/2021.05.24.445376

Dataset Information#

Dataset ID	`DS004043`
Title	The time-course of feature-based attention effects dissociated from temporal expectation and target-related processes
Year	2022
Authors	Moerel, Denise, Grootswagers, Tijl, Robinson, Amanda, Shatek, Sophia, Woolgar, Alexandra, Carlson, Thomas, Rich, Anina
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004043.v1.1.0
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: 20
Recordings: 84
Tasks: 1

Channels & sampling rate

Channels: 63
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS004043 class to access this dataset programmatically.

class eegdash.dataset.DS004043(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004043. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 20; recordings: 20; 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/ds004043 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004043

Examples

>>> from eegdash.dataset import DS004043
>>> dataset = DS004043(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