ON007431: eeg dataset, 47 subjects#
Diffuse predictions stabilize and reshape neural code during memory encoding
Access recordings and metadata through EEGDash.
Citation: Nursena Ataseven, Sahcan Ozdemir, Wouter Kruijne, Daniel Schneider, Elkan G. Akyurek (—). Diffuse predictions stabilize and reshape neural code during memory encoding. 10.82901/nemar.on007431
Modality: eeg Subjects: 47 Recordings: 47 License: CC0 Source: nemar
Metadata: Good (80%)
47-participant EEG dataset — Diffuse predictions stabilize and reshape neural code during memory encoding.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON007431
dataset = ON007431(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON007431(cache_dir="./data", subject="01")
Advanced query
dataset = ON007431(
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{on007431,
title = {Diffuse predictions stabilize and reshape neural code during memory encoding},
author = {Nursena Ataseven and Sahcan Ozdemir and Wouter Kruijne and Daniel Schneider and Elkan G. Akyurek},
doi = {10.82901/nemar.on007431},
url = {https://doi.org/10.82901/nemar.on007431},
}
About This Dataset#
Experimental task: participants judged whether a probe grating was rotated clockwise or counterclockwise relative to a memorized orientation, which was either predictable or unpredictable. Each memory item was preceded by a central color cue (red, green, or blue). In half of the trials, two of these colors (predictive) cued two non-overlapping 90° segments of orientations that the grating was sampled from. Thus, participants knew the range of possible orientations of these items, but not their exact orientation. In the other half of the trials, a third (non-predictive) color was presented that signaled the item could have any possible orientation.The preprocessing and analysis scripts can be found on OSF: https://osf.io/8evwh/
Cohort#
Signal · Electrodes & live trace#
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 |
Diffuse predictions stabilize and reshape neural code during memory encoding |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Nursena Ataseven, Sahcan Ozdemir, Wouter Kruijne, Daniel Schneider, Elkan G. Akyurek |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on007431,
title = {Diffuse predictions stabilize and reshape neural code during memory encoding},
author = {Nursena Ataseven and Sahcan Ozdemir and Wouter Kruijne and Daniel Schneider and Elkan G. Akyurek},
doi = {10.82901/nemar.on007431},
url = {https://doi.org/10.82901/nemar.on007431},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON007431(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Diffuse predictions stabilize and reshape neural code during memory encoding
- Study:
on007431(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
ON007431.Modality:
eeg; Subject type:Unknown. Subjects: 47; recordings: 47; 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/on007431 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on007431 DOI: https://doi.org/10.82901/nemar.on007431
Examples
>>> from eegdash.dataset import ON007431 >>> dataset = ON007431(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.pytorch
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 on007431 to reproduce the tutorial on this dataset.
Citation
Nursena Ataseven, Sahcan Ozdemir, Wouter Kruijne, Daniel Schneider, Elkan G. Akyurek (n.d.). Diffuse predictions stabilize and reshape neural code during memory encoding. 10.82901/nemar.on007431
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on007431.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset