DS001787: eeg dataset, 24 subjects#

EEG meditation study

Access recordings and metadata through EEGDash.

Citation: Arnaud Delorme, Tracy Brandmeyer (2019). EEG meditation study. 10.18112/openneuro.ds001787.v1.1.1

Modality: eeg Subjects: 24 Recordings: 40 License: CC0 Source: openneuro Citations: 6.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS001787

dataset = DS001787(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 = DS001787(cache_dir="./data", subject="01")

Advanced query

dataset = DS001787(
    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{ds001787,
  title = {EEG meditation study},
  author = {Arnaud Delorme and Tracy Brandmeyer},
  doi = {10.18112/openneuro.ds001787.v1.1.1},
  url = {https://doi.org/10.18112/openneuro.ds001787.v1.1.1},
}

About This Dataset#

This meditation experiment contains 24 subjects. Subjects were meditating and were interupted about every 2 minutes to indicate their level of concentration and mind wandering. The scientific article (see Reference) contains all methodological details. Note that although the original files were recorded at 2048 Hz, they were downsampled to 256 Hz using the BDF decimator provided by BIOSEMI (https://www.biosemi.com/download.htm). - Arnaud Delorme (October 17, 2018; updated June 2024)

Dataset Information#

Dataset ID	`DS001787`
Title	EEG meditation study
Author (year)	`Delorme2019`
Canonical	—
Importable as	`DS001787`, `Delorme2019`
Year	2019
Authors	Arnaud Delorme, Tracy Brandmeyer
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds001787.v1.1.1
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: 24
Recordings: 40
Tasks: 1

Channels & sampling rate

Channels: 79
Sampling rate (Hz): 256.0
Duration (hours): 27.607222222222223

Electrode Layout#

Electrode layout — EEG · 64 sensors — 64 channels

Dataset Statistics#

Age distribution (n=23, range 29–78 yr)

253035404550606575

Sex distribution

12

Female Male Total: 24

Channel counts: 79 ch (n=40 recordings)

Sampling frequencies: 256.0 Hz (n=40 recordings)

Total recording duration: 27 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

Signal Preview#

Live trace viewer — sub-021 · ses-01 · task-meditation

Showing one representative recording out of 24 subjects and 40 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.

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 DS001787 class to access this dataset programmatically.

class eegdash.dataset.DS001787(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

EEG meditation study

Study:: ds001787 (OpenNeuro)
Author (year):: Delorme2019
Canonical:: —

Also importable as: DS001787, Delorme2019.

Modality: eeg. Subjects: 24; recordings: 40; tasks: 1.

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/ds001787 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds001787 DOI: https://doi.org/10.18112/openneuro.ds001787.v1.1.1 NEMAR citation count: 6

Examples

>>> from eegdash.dataset import DS001787
>>> dataset = DS001787(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)