DS004572: eeg dataset, 52 subjects#
The effects of sham hypnosis techniques
Citation: Zoltan Kekecs, Kyra Girán, Vanda Vizkievicz, Anna Lutoskin, Yeganeh Farahzadi (—). The effects of sham hypnosis techniques. 10.18112/openneuro.ds004572.v1.3.2
52-participant EEG dataset — The effects of sham hypnosis techniques.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004572
dataset = DS004572(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004572(cache_dir="./data", subject="01")
Advanced query
dataset = DS004572(
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{ds004572,
title = {The effects of sham hypnosis techniques},
author = {Zoltan Kekecs and Kyra Girán and Vanda Vizkievicz and Anna Lutoskin and Yeganeh Farahzadi},
doi = {10.18112/openneuro.ds004572.v1.3.2},
url = {https://doi.org/10.18112/openneuro.ds004572.v1.3.2},
}
About This Dataset#
52 participants (39 females) took part in this study and their brain electrophysiological activity were being recorded using 64-channel EasyCap from Brain Products. After mounting the EEG electrode cap, the study protocol started with 5 minutes of closed-eyes rest (Pre-hypnosis Baseline), followed by four experimental conditions (Experimental Blocks), and ended with another 5 minutes of closed-eyes rest (Post-hypnosis Baseline). Throughout the four Experimental Blocks, participants were exposed to either conventional or unconventional (placebo) hypnotic inductions described either as hypnosis or as simple relaxation technique in a 2 x 2 balanced placebo design. In other words, each participant underwent four trials, in which they were exposed to a conventional hypnosis induction presented as “hypnosis”; a conventional hypnosis induction presented as “control”; an unconventional hypnosis induction presented as “hypnosis”; and an unconventional hypnosis induction presented as “control” in a randomized order.
For detailed information on our data collection methods, refer to the public trial registry on the Open Science Framework: https://doi.org/10.17605/OSF.IO/WVHDA.
Publications based on this dataset: - https://onlinelibrary.wiley.com/doi/full/10.1111/psyp.70183 - https://www.nature.com/articles/s41598-024-56633-x
Cohort#
Dataset Statistics#
Channel counts: 61 ch (n=516 recordings)
Sampling frequencies: 1000.0 Hz (n=516 recordings)
Total recording duration: 53 h
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · ses-01 · task-induction1
Showing one representative recording out of
52 subjects and 516 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 · 58 sensors — 58 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 |
The effects of sham hypnosis techniques |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Zoltan Kekecs, Kyra Girán, Vanda Vizkievicz, Anna Lutoskin, Yeganeh Farahzadi |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004572,
title = {The effects of sham hypnosis techniques},
author = {Zoltan Kekecs and Kyra Girán and Vanda Vizkievicz and Anna Lutoskin and Yeganeh Farahzadi},
doi = {10.18112/openneuro.ds004572.v1.3.2},
url = {https://doi.org/10.18112/openneuro.ds004572.v1.3.2},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004572 · Kekecs2023eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004572(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
The effects of sham hypnosis techniques
- Study:
ds004572(OpenNeuro)- Author (year):
Kekecs2023- Canonical:
—
Also importable as:
DS004572,Kekecs2023.Modality:
eeg; Experiment type:Other; Subject type:Healthy. Subjects: 52; recordings: 516; tasks: 10.- 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/ds004572 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004572 DOI: https://doi.org/10.18112/openneuro.ds004572.v1.3.2 NEMAR citation count: 2
Examples
>>> from eegdash.dataset import DS004572 >>> dataset = DS004572(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/ds004572").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 ds004572 to reproduce the tutorial on this dataset.
Citation
Zoltan Kekecs, Kyra Girán, Vanda Vizkievicz, Anna Lutoskin, Yeganeh Farahzadi (n.d.). The effects of sham hypnosis techniques. 10.18112/openneuro.ds004572.v1.3.2
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004572.v1.3.2.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset