DS005586: eeg dataset, 23 subjects#
Electroencephalographic responses to the number of objects in partially occluded and uncovered scenes
Citation: Cemre Baykan, Alexander C. Schütz (—). Electroencephalographic responses to the number of objects in partially occluded and uncovered scenes. 10.18112/openneuro.ds005586.v2.0.0
23-participant EEG dataset — Electroencephalographic responses to the number of objects in partially occluded and uncovered scenes.
Quickstart#
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)
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'])
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#
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.
Passing Viewing Task
Cohort#
Dataset Statistics#
Age distribution by gender (n=23, range 19–44 yr, mean 23.6 yr)
Sex composition
Channel counts: 63 ch (n=23 recordings)
Sampling frequencies: 1000.0 Hz (n=23 recordings)
Total recording duration: 33 h
Signal · Electrodes & live trace#
Live trace viewer — sub-021 · task-PassiveViewing
Showing one representative recording out of
23 subjects and 23 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
Electrode layout — EEG · 60 sensors — 60 channels
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
Manifest#
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.
Full dataset metadata table
Dataset ID |
|
Title |
Electroencephalographic responses to the number of objects in partially occluded and uncovered scenes |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Cemre Baykan, Alexander C. Schütz |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005586 · Baykan2024eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005586(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand 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)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds005586").huggingface
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for ds005586 to reproduce the tutorial on this dataset.
Citation
Cemre Baykan, Alexander C. Schütz (n.d.). Electroencephalographic responses to the number of objects in partially occluded and uncovered scenes. 10.18112/openneuro.ds005586.v2.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds005586.v2.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset