DS005586: eeg dataset, 23 subjects#

Electroencephalographic responses to the number of objects in partially occluded and uncovered scenes

Access recordings and metadata through EEGDash.

Citation: Cemre Baykan, Alexander C. Schütz (2024). Electroencephalographic responses to the number of objects in partially occluded and uncovered scenes. 10.18112/openneuro.ds005586.v2.0.0

Modality: eeg Subjects: 23 Recordings: 23 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005586

dataset = DS005586(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 = DS005586(cache_dir="./data", subject="01")

Advanced query

dataset = DS005586(
    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{ds005586,
  title = {Electroencephalographic responses to the number of objects in partially occluded and uncovered scenes},
  author = {Cemre Baykan and Alexander C. Schütz},
  doi = {10.18112/openneuro.ds005586.v2.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005586.v2.0.0},
}

About This Dataset#

Passing Viewing Task 23 participants took part in this study in return for a monetary incentive at University of Marburg. Participants performed a passive viewing task in a dimly lit room. The visual scene consisted of a game board, game pieces and a mesh as an occluder. Each trial started with a fixation cross presentation for one second plus the duration of the drift correction procedure. The game board and occluder were presented for two seconds, while game pieces only appeared in the last one second of this presentation. Following the “partially occluded scene”, the occluder disappeared to uncover the hidden parts of the game board along with the visible game pieces leading to the “uncovered scene” phase. The uncovered scene was presented for one second. The experiment consisted of eight blocks of 80 trials each. There were two conditions of initially visible game pieces: 4 or 32 pieces, each with 8 uncovered conditions: 0, 1, 2, 4, 28, 30, 31 or 32 uncovered game pieces. All 16 conditions were repeated 40 times during the experiment, summing up to 640 trials in total. Participants 9, 10 and 15 were excluded from the analyses due to excessive head movements and equipment malfunction.

Dataset Information#

Dataset ID	`DS005586`
Title	Electroencephalographic responses to the number of objects in partially occluded and uncovered scenes
Author (year)	`Baykan2024`
Canonical	—
Importable as	`DS005586`, `Baykan2024`
Year	2024
Authors	Cemre Baykan, Alexander C. Schütz
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005586.v2.0.0
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: 23
Recordings: 23
Tasks: 1

Channels & sampling rate

Channels: 63
Sampling rate (Hz): 1000.0
Duration (hours): 33.52867694444444

Electrode Layout#

Electrode layout — EEG · 60 sensors — 60 channels

Dataset Statistics#

Age distribution (n=23, range 19–44 yr)

1520253040

Sex distribution

15

8

Female Male Total: 23

Channel counts: 63 ch (n=23 recordings)

Sampling frequencies: 1000.0 Hz (n=23 recordings)

Total recording duration: 33 h

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the DS005586 class to access this dataset programmatically.

class eegdash.dataset.DS005586(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Electroencephalographic responses to the number of objects in partially occluded and uncovered scenes

Study:: ds005586 (OpenNeuro)
Author (year):: Baykan2024
Canonical:: —

Also importable as: DS005586, Baykan2024.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 23; recordings: 23; 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/ds005586 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005586 DOI: https://doi.org/10.18112/openneuro.ds005586.v2.0.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005586
>>> dataset = DS005586(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)