DS001787#

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: 141 License: CC0 Source: openneuro Citations: 6.0

Metadata: Complete (100%)

Quickstart#

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)

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'])

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

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
Copy-paste 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},
}

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: 141

  • Tasks: 1

Channels & sampling rate

  • Channels: 64

  • Sampling rate (Hz): 256.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 5.7 GB

  • File count: 141

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds001787.v1.1.1

Provenance

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

OpenNeuro dataset ds001787. Modality: eeg; Experiment type: Attention; Subject type: Healthy. 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

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, 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

See Also#