DS005189: eeg dataset, 30 subjects#
Search Superiority Recollection Familiarity
Citation: Jason Helbing, Dejan Draschkow, Melissa L.-H. Võ (—). Search Superiority Recollection Familiarity. 10.18112/openneuro.ds005189.v1.0.1
30-participant EEG dataset — Search Superiority Recollection Familiarity.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS005189
dataset = DS005189(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS005189(cache_dir="./data", subject="01")
Advanced query
dataset = DS005189(
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{ds005189,
title = {Search Superiority Recollection Familiarity},
author = {Jason Helbing and Dejan Draschkow and Melissa L.-H. Võ},
doi = {10.18112/openneuro.ds005189.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds005189.v1.0.1},
}
About This Dataset#
In this experiment, participants searched for objects in some scenes and intentionally memorized others. We then tested their memory of these objects, finding stronger (quantitative difference) and different (qualitative difference: recollection benefit) memory representations for search targets.
We recorded both EEG and eye movements. Behavioral data is split into encoding (Encode_beh) and memory testing (Test_beh).
Analysis scripts and preprocessed data as well as additional materials are available on the OSF at https://osf.io/esr5q/.
Project Abstract:
Most memory is not formed deliberately but as a by-product of natural behavior. These incidental representations, when generated during visual search, can be stronger than intentionally memorized content (search superiority effect). In this study, we investigate whether this effect is purely quantitative (stronger memory) or also due to qualitative memory differences; more precisely, differences in recollection and familiarity, two processes supporting recognition memory. In an EEG study with eye tracking, 30 participants searched for objects in scenes and intentionally memorized others before completing a surprise recognition memory test. We find that compared to new objects, both search targets and intentionally memorized objects elicit a more positive-going mid-frontal negativity peaking at around 400 ms post stimulus onset (FN400), which is associated with familiarity, as well as a more positive-going parietal late component (LPC), indicative of recollection. Both components show no differences between tasks, indicating equal contributions of recollection and familiarity to remembering searched and memorized objects. Behavioral data from remember–know judgments and receiver operating characteristics (ROCs), however, contrasts with the EEG findings: Search targets are more often reported as recollected and their ROCs show higher intercepts, indicating more recollection, whereas there are essentially no behavioral differences in familiarity between tasks. These results indicate that search superiority relies on increased recollection rather than familiarity. The absent LPC effect despite the behavioral task difference challenges existing assumptions about the neural correlates of recognition memory, raising the question whether they hold when investigated using real-world scenes and incidental encoding during naturalistic tasks.
Cohort#
Dataset Statistics#
Age distribution by gender (n=30, range 18–34 yr, mean 22.7 yr)
Sex composition
Channel counts: 62 ch (n=30 recordings)
Sampling frequencies: 1000.0 Hz (n=30 recordings)
Total recording duration: 19 h 17 min
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-SearchSupRecFam
Showing one representative recording out of
30 subjects and 30 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.
No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.
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 |
Search Superiority Recollection Familiarity |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Jason Helbing, Dejan Draschkow, Melissa L.-H. Võ |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds005189,
title = {Search Superiority Recollection Familiarity},
author = {Jason Helbing and Dejan Draschkow and Melissa L.-H. Võ},
doi = {10.18112/openneuro.ds005189.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds005189.v1.0.1},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005189 · Helbing2024eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005189(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Search Superiority Recollection Familiarity
- Study:
ds005189(OpenNeuro)- Author (year):
Helbing2024- Canonical:
—
Also importable as:
DS005189,Helbing2024.Modality:
eeg. Subjects: 30; recordings: 30; 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/ds005189 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005189 DOI: https://doi.org/10.18112/openneuro.ds005189.v1.0.1 NEMAR citation count: 0
Examples
>>> from eegdash.dataset import DS005189 >>> dataset = DS005189(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/ds005189").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 ds005189 to reproduce the tutorial on this dataset.
Citation
Jason Helbing, Dejan Draschkow, Melissa L.-H. Võ (n.d.). Search Superiority Recollection Familiarity. 10.18112/openneuro.ds005189.v1.0.1
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds005189.v1.0.1.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset