DS004572: eeg dataset, 52 subjects#
The effects of sham hypnosis techniques
Access recordings and metadata through EEGDash.
Citation: Zoltan Kekecs, Kyra Girán, Vanda Vizkievicz, Anna Lutoskin, Yeganeh Farahzadi (2023). The effects of sham hypnosis techniques. 10.18112/openneuro.ds004572.v1.3.2
Modality: eeg Subjects: 52 Recordings: 516 License: CC0 Source: openneuro Citations: 2.0
Metadata: Complete (100%)
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
Dataset Information#
Dataset ID |
|
Title |
The effects of sham hypnosis techniques |
Author (year) |
|
Canonical |
|
Importable as |
|
Year |
2023 |
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},
}
Found an issue with this dataset?
If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!
Technical Details#
Subjects: 52
Recordings: 516
Tasks: 10
Channels: 61
Sampling rate (Hz): 1000.0
Duration (hours): 53.24708222222222
Pathology: Healthy
Modality: Auditory
Type: Other
Size on disk: 43.6 GB
File count: 516
Format: BIDS
License: CC0
DOI: doi:10.18112/openneuro.ds004572.v1.3.2
API Reference#
Use the DS004572 class to access this dataset programmatically.
- class eegdash.dataset.DS004572(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Bases:
EEGDashDatasetThe effects of sham hypnosis techniques
- Study:
ds004572(OpenNeuro)- Author (year):
Kekecs2023- Canonical:
Kekecs2024
Also importable as:
DS004572,Kekecs2023,Kekecs2024.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
- query#
Merged query with the dataset filter applied.
- Type:
dict
- records#
Metadata records used to build the dataset, if pre-fetched.
- Type:
list[dict] | None
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()
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset