DS003969: eeg dataset, 98 subjects#
Meditation vs thinking task
Citation: Arnaud Delorme, Claire Braboszcz (—). Meditation vs thinking task. 10.18112/openneuro.ds003969.v1.0.0
98-participant EEG dataset — Meditation vs thinking task.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS003969
dataset = DS003969(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS003969(cache_dir="./data", subject="01")
Advanced query
dataset = DS003969(
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{ds003969,
title = {Meditation vs thinking task},
author = {Arnaud Delorme and Claire Braboszcz},
doi = {10.18112/openneuro.ds003969.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds003969.v1.0.0},
}
About This Dataset#
Data collection took place at the Meditation Research Institute (MRI) in Rishikesh, India, under the supervision of Arnaud Delorme, Ph.D. The project was approved by the local MRI Indian ethical committee and the ethical committee of the University of California San Diego (IRB project # 090731).
Participants sat either on a blanket on the floor or on a chair for both experimental periods depending on their personal preference. Participants were asked to keep their eyes closed, and all lighting in the room was turned off during data collection. An intercom allowed communication between the experimental and the recording room.
Participants performed four blocks, 2 meditation blocks interspaced by two thining blocks (in which they are instructed to think actively). Half of the participants start with a meditation block, and half of them start with a thinking block. The first meditation block is a breath counting meditation for all participants. The second block is a tradition-specific meditation - except for the control group, for which it is a breath counting meditation.
Cohort#
Dataset Statistics#
Age distribution by gender (n=97, range 22–74 yr, mean 43.9 yr)
Sex composition
Channel counts (ch)
Sampling frequencies (Hz)
Total recording duration: 66 h
Signal · Electrodes & live trace#
Live trace viewer — sub-021 · task-think1
Showing one representative recording out of
98 subjects and 392 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 |
Meditation vs thinking task |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Arnaud Delorme, Claire Braboszcz |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds003969,
title = {Meditation vs thinking task},
author = {Arnaud Delorme and Claire Braboszcz},
doi = {10.18112/openneuro.ds003969.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds003969.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS003969 · Delorme2021eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS003969(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Meditation vs thinking task
- Study:
ds003969(OpenNeuro)- Author (year):
Delorme2021- Canonical:
—
Also importable as:
DS003969,Delorme2021.Modality:
eeg. Subjects: 98; recordings: 392; tasks: 4.- 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/ds003969 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003969 DOI: https://doi.org/10.18112/openneuro.ds003969.v1.0.0 NEMAR citation count: 7
Examples
>>> from eegdash.dataset import DS003969 >>> dataset = DS003969(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/ds003969").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 ds003969 to reproduce the tutorial on this dataset.
Citation
Arnaud Delorme, Claire Braboszcz (n.d.). Meditation vs thinking task. 10.18112/openneuro.ds003969.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds003969.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset