DS003004: eeg dataset, 34 subjects#
Imagined Emotion Study
Citation: Julie Onton, Scott Makeig (—). Imagined Emotion Study. 10.18112/openneuro.ds003004.v1.1.1
34-participant EEG dataset — Imagined Emotion Study.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS003004
dataset = DS003004(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS003004(cache_dir="./data", subject="01")
Advanced query
dataset = DS003004(
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{ds003004,
title = {Imagined Emotion Study},
author = {Julie Onton and Scott Makeig},
doi = {10.18112/openneuro.ds003004.v1.1.1},
url = {https://doi.org/10.18112/openneuro.ds003004.v1.1.1},
}
About This Dataset#
PARADIGM: The study uses the method of guided imagery to induce resting, eyes-closed participants using voice-guided imagination to enter distinct 15 emotion states during acquisition of high-density EEG data.
During the study, participants listen to 15 voice recordings that each suggest imagining a scenario in which they have experienced – or would experience the named target emotion. Some target emotions have positive valence (e.g., joy, happiness), others negative valence (e.g., sadness, anger). Before and between the 15 emotion imagination periods, participants hear relaxation suggestions (‘Now return to a neutral state by …’).
PROCEDURE: When the participant first begins to feel the target emotion, they are asked to indicate this by pressing a handheld button. Participants are asked to continue feeling the emotion as long as possible. To intensify and lengthen the periods of experienced emotion, participants are asked to interoceptively perceive and attend relevant somatosensory sensations. When the target feeling wanes (typically after 1 and 5 minutes), participants push the button again to leave the emotion imagination period and cue the relaxation instructions. DATA HANDLING: The raw data have been preprocessed to fix confusing event codes and to remove excessively noisy channels. In addition, a 1-Hz high pass filter was applied to ready the data for ICA decomposition. Note: Unfortunately, the unfiltered data are no longer available. NOTE: Sub22 was a repeat subject, hence was removed from the dataset.
Cohort#
Dataset Statistics#
Age distribution (n=34, range 18–38 yr, mean 25.2 yr · sex per subject not reported)
Sex composition
Channel counts (ch)
Sampling frequencies: 256.0 Hz (n=34 recordings)
Total recording duration: 49 h
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-ImaginedEmotion
Showing one representative recording out of
34 subjects and 34 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 · 229 sensors — 229 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 |
Imagined Emotion Study |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Julie Onton, Scott Makeig |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds003004,
title = {Imagined Emotion Study},
author = {Julie Onton and Scott Makeig},
doi = {10.18112/openneuro.ds003004.v1.1.1},
url = {https://doi.org/10.18112/openneuro.ds003004.v1.1.1},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003004 · Onton2020eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003004(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Imagined Emotion Study
- Study:
ds003004(OpenNeuro)- Author (year):
Onton2020- Canonical:
—
Also importable as:
DS003004,Onton2020.Modality:
eeg. Subjects: 34; recordings: 34; 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/ds003004 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003004 DOI: https://doi.org/10.18112/openneuro.ds003004.v1.1.1 NEMAR citation count: 7
Examples
>>> from eegdash.dataset import DS003004 >>> dataset = DS003004(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/ds003004").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 ds003004 to reproduce the tutorial on this dataset.
Citation
Julie Onton, Scott Makeig (n.d.). Imagined Emotion Study. 10.18112/openneuro.ds003004.v1.1.1
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds003004.v1.1.1.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset