DS004262: eeg dataset, 21 subjects#
Continuous Feedback Processing
Citation: Cameron D. Hassall, Yan Yan, Laurence T. Hunt (—). Continuous Feedback Processing. 10.18112/openneuro.ds004262.v1.0.0
21-participant EEG dataset — Continuous Feedback Processing.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004262
dataset = DS004262(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004262(cache_dir="./data", subject="01")
Advanced query
dataset = DS004262(
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{ds004262,
title = {Continuous Feedback Processing},
author = {Cameron D. Hassall and Yan Yan and Laurence T. Hunt},
doi = {10.18112/openneuro.ds004262.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004262.v1.0.0},
}
About This Dataset#
Twenty-one participants learned to predict the final level of an animated rising bar. Following the appearance of a fixation cross, participants used the mouse to indicate their guess (i.e., how high they thought the bar would rise). After a delay, participants watched the bar rise to its final level. Points were awarded based on the distance between their guess and the actual level. Each round was cued by the appearance of a gnome (cover story: the gnomes are playing a strongman game while visiting a fair). Cues varied in the degree to which the outcome was predictable (highly predictable, somewhat predictable, unpredictable).
Participant 11 was excluded from the analysis due to excessive artifacts.
Timing fixation cross (400-600 ms) -> gnome cue (1500 ms) -> bar outline (until response) -> animation (1 degree per second until complete) -> final outcome (1000 ms) Conditions (Gnome Types) 1: highly predictable - consistently low 2: highly predictable - consistently high 3: unpredictable - low or high with equal probability 4: somewhat predictable - usually (80%) low, sometimes high 5: somewhat predictable - usually (80%) high, sometimes low 6: unpredictable - random uniform distribution Trigger Modifiers Add 0: Fixation cross Add 10: Cue (gnome) onset Add 20: Bar outline appears Add 30: Participant response Add 40: Start of animation Add 50: End of animation (and start of 1-second delay)
Continuous Feedback Processing
Cohort#
Dataset Statistics#
Age distribution by gender (n=21, range 21–41 yr, mean 25.8 yr)
Sex composition
Channel counts: 31 ch (n=21 recordings)
Sampling frequencies: 1000.0 Hz (n=21 recordings)
Total recording duration: 8 h 20 min
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-gnomes
Showing one representative recording out of
21 subjects and 21 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 · 31 sensors — 31 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 |
Continuous Feedback Processing |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Cameron D. Hassall, Yan Yan, Laurence T. Hunt |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004262,
title = {Continuous Feedback Processing},
author = {Cameron D. Hassall and Yan Yan and Laurence T. Hunt},
doi = {10.18112/openneuro.ds004262.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004262.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004262 · Hassall2022_Continuouseegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004262(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Continuous Feedback Processing
- Study:
ds004262(OpenNeuro)- Author (year):
Hassall2022_Continuous- Canonical:
—
Also importable as:
DS004262,Hassall2022_Continuous.Modality:
eeg. Subjects: 21; recordings: 21; 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/ds004262 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004262 DOI: https://doi.org/10.18112/openneuro.ds004262.v1.0.0 NEMAR citation count: 1
Examples
>>> from eegdash.dataset import DS004262 >>> dataset = DS004262(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/ds004262").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 ds004262 to reproduce the tutorial on this dataset.
Citation
Cameron D. Hassall, Yan Yan, Laurence T. Hunt (n.d.). Continuous Feedback Processing. 10.18112/openneuro.ds004262.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004262.v1.0.0.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset