DS004572#

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.v1.3.1

Modality: eeg Subjects: 52 Recordings: 3153 License: CC0 Source: openneuro Citations: 2.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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.v1.3.1},
  url = {https://doi.org/10.18112/openneuro.ds004572.v1.3.1},
}

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://www.nature.com/articles/s41598-024-56633-x

https://osf.io/preprints/psyarxiv/k3h5f

Dataset Information#

Dataset ID	`DS004572`
Title	The effects of sham hypnosis techniques
Year	2023
Authors	Zoltan Kekecs, Kyra Girán, Vanda Vizkievicz, Anna Lutoskin, Yeganeh Farahzadi
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004572.v1.3.1
Source links	OpenNeuro \| NeMAR \| Source URL

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!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 52
Recordings: 3153
Tasks: 10

Channels & sampling rate

Channels: 61 (516), 58 (516)
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

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: EEGDashDataset

OpenNeuro dataset ds004572. Modality: eeg; Experiment type: Perception. 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. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and 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

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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None