DS003969: eeg dataset, 98 subjects#

Meditation vs thinking task

Access recordings and metadata through EEGDash.

Citation: Arnaud Delorme, Claire Braboszcz (2021). Meditation vs thinking task. 10.18112/openneuro.ds003969.v1.0.0

Modality: eeg Subjects: 98 Recordings: 392 License: CC0 Source: openneuro Citations: 7.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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.

Dataset Information#

Dataset ID	`DS003969`
Title	Meditation vs thinking task
Author (year)	`Delorme2021`
Canonical	—
Importable as	`DS003969`, `Delorme2021`
Year	2021
Authors	Arnaud Delorme, Claire Braboszcz
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds003969.v1.0.0
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: 98
Recordings: 392
Tasks: 4

Channels & sampling rate

Channels: 79 (294), 72 (98)
Sampling rate (Hz): 1024.0 (386), 2048.0 (6)
Duration (hours): 66.53361111111111

Electrode Layout#

Electrode layout — EEG · 64 sensors — 64 channels

Dataset Statistics#

Age distribution (n=97, range 22–74 yr)

2025303540455055606570

Sex distribution

26

72

Female Male Total: 98

Channel counts (ch)

7279

Sampling frequencies (Hz)

10242048

Total recording duration: 66 h

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the DS003969 class to access this dataset programmatically.

class eegdash.dataset.DS003969(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

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

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/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)