DS006437: eeg dataset, 9 subjects#
LIGHT Hypnotherapy
Citation: anonymous (—). LIGHT Hypnotherapy. 10.18112/openneuro.ds006437.v1.1.0
9-participant EEG dataset — LIGHT Hypnotherapy.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS006437
dataset = DS006437(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS006437(cache_dir="./data", subject="01")
Advanced query
dataset = DS006437(
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{ds006437,
title = {LIGHT Hypnotherapy},
author = {anonymous},
doi = {10.18112/openneuro.ds006437.v1.1.0},
url = {https://doi.org/10.18112/openneuro.ds006437.v1.1.0},
}
About This Dataset#
LIGHT Dataset
During these guided imagery hypnotherapy sessions, a hypnotherapist integrates storytelling and deep visualizations techniques to encourage personal and wellbeing. This method allows individuals to explore and modify their subconscious narratives, in order to foster changes that resonate through their lives. This differs from traditional hypnotherapy by tapping into eudaemonic aspects of well-being, emphasizing the pursuit of meaning and self realization. 2 weeks before the first session, they will come to the lab to record baseline resting EEG (session 0).
With the exception of sessions 1, 4, and 8, all LIGHT
sessions were performed virtually. For sessions 1, 4, and 8, EEG and ECG data was collected.
The first phase of each session was guided relaxation or induction followed by prompts to visualize a perfect place of comfort and safely in ones creative imagination. Next, they were prompted to visualize a path or a set of stairs, and asked to walk down that path or stairway for ten steps, counting down from 10 to 1 outloud. They were guided to visualize and describe a chair or seat, followed by a crown.
After taking a seat in their imagined chair and
View full README
The first phase of each session was guided relaxation or induction followed by prompts to visualize a perfect place of comfort and safely in ones creative imagination. Next, they were prompted to visualize a path or a set of stairs, and asked to walk down that path or stairway for ten steps, counting down from 10 to 1 outloud. They were guided to visualize and describe a chair or seat, followed by a crown.
After taking a seat in their imagined chair and putting on the crown, they were encouraged to pause and observe the world they had conjured. Following this brief period of reflection, awareness was drawn to a light or color that emerged from their creative mind. They identified the color of the light and were instructed to imagine that light entering and traveling through their body, filling up each cell as it passed through. The participant was instructed to leave a mental bookmark in this place so that they could come back to it at any time, before removing and storing their crown. At the end of the LIGHT sessions, the participant was asked to rise from their imagined chair or throne and count their way up from one to ten, with the session ending as the participant returned to an awake and alert state at the end of the ten count.
Baseline recordings were 5 minute of length. Hypnotherapy recordings are variable in length with multiple events indicating a hypnotherapist phase transitions.
Cohort#
Dataset Statistics#
Age distribution by gender (n=9, range 25–66 yr, mean 47.0 yr)
Sex composition
Channel counts: 64 ch (n=63 recordings)
Sampling frequencies: 256.0 Hz (n=63 recordings)
Total recording duration: 16 h 47 min
Signal · Electrodes & live trace#
Live trace viewer — sub-002 · ses-4 · task-baseline4
Showing one representative recording out of
9 subjects and 63 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 · 64 sensors — 64 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 |
LIGHT Hypnotherapy |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
anonymous |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds006437,
title = {LIGHT Hypnotherapy},
author = {anonymous},
doi = {10.18112/openneuro.ds006437.v1.1.0},
url = {https://doi.org/10.18112/openneuro.ds006437.v1.1.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS006437 · DS6437_LIGHT_Hypnotherapyeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS006437(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
LIGHT Hypnotherapy
- Study:
ds006437(OpenNeuro)- Author (year):
DS6437_LIGHT_Hypnotherapy- Canonical:
—
Also importable as:
DS006437,DS6437_LIGHT_Hypnotherapy.Modality:
eeg; Experiment type:Clinical/Intervention; Subject type:Healthy. Subjects: 9; recordings: 63; tasks: 5.- 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/ds006437 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006437 DOI: https://doi.org/10.18112/openneuro.ds006437.v1.1.0
Examples
>>> from eegdash.dataset import DS006437 >>> dataset = DS006437(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/ds006437").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 ds006437 to reproduce the tutorial on this dataset.
Citation
anonymous (n.d.). LIGHT Hypnotherapy. 10.18112/openneuro.ds006437.v1.1.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds006437.v1.1.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset