DS004252#

Rotation-tolerant representations elucidate the time course of high-level object processing

Access recordings and metadata through EEGDash.

Citation: Denise Moerel, Tijl Grootswagers, Amanda K. Robinson, Patrick Engeler, Alex O. Holcombe, Thomas A. Carlson (2022). Rotation-tolerant representations elucidate the time course of high-level object processing. 10.18112/openneuro.ds004252.v1.0.2

Modality: eeg Subjects: 1 Recordings: 7 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004252

dataset = DS004252(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 = DS004252(cache_dir="./data", subject="01")

Advanced query

dataset = DS004252(
    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{ds004252,
  title = {Rotation-tolerant representations elucidate the time course of high-level object processing},
  author = {Denise Moerel and Tijl Grootswagers and Amanda K. Robinson and Patrick Engeler and Alex O. Holcombe and Thomas A. Carlson},
  doi = {10.18112/openneuro.ds004252.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds004252.v1.0.2},
}

About This Dataset#

Note: only the data for participant 1 has been uploaded. The rest of the dataset will be released upon publication.

The pre-print can be found here: https://doi.org/10.31234/osf.io/wp73u The analysis codes, results, and figures can be found on OSF: https://osf.io/r93es.

The main folder contains the raw EEG data in standard bids format.

The ‘derivatives’ folder contains the pre-processed & epoched EEG data, formatted in line with cosmomvpa.

For codes, results, & figures, see OSF: Engeler, P., Grootswagers, T., Robinson, A. K., Holcombe, A. O., Carlson, T. A., & Moerel, D. (2022, August 17). Rotation-tolerant representations elucidate the time course of high-level object processing. Retrieved from osf.io/r93es

Dataset Information#

Dataset ID	`DS004252`
Title	Rotation-tolerant representations elucidate the time course of high-level object processing
Year	2022
Authors	Denise Moerel, Tijl Grootswagers, Amanda K. Robinson, Patrick Engeler, Alex O. Holcombe, Thomas A. Carlson
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004252.v1.0.2
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: 1
Recordings: 7
Tasks: —

Channels & sampling rate

Channels: 127
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS004252 class to access this dataset programmatically.

class eegdash.dataset.DS004252(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004252. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 1; recordings: 1; 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/ds004252 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004252

Examples

>>> from eegdash.dataset import DS004252
>>> dataset = DS004252(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