DS003816#

The Effect of Buddhism Derived Loving Kindness Meditation on Modulating EEG: Long-term and Short-term Effect

Access recordings and metadata through EEGDash.

Citation: SUN, Rui, Ven WONG, Goon Fui, GAO, Jungling (—). The Effect of Buddhism Derived Loving Kindness Meditation on Modulating EEG: Long-term and Short-term Effect. 10.18112/openneuro.ds003816.v1.0.1

Modality: eeg Subjects: 48 Recordings: 1077 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003816

dataset = DS003816(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 = DS003816(cache_dir="./data", subject="01")

Advanced query

dataset = DS003816(
    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{ds003816,
  title = {The Effect of Buddhism Derived Loving Kindness Meditation on Modulating EEG: Long-term and Short-term Effect},
  author = {SUN, Rui and Ven WONG, Goon Fui and GAO, Jungling},
  doi = {10.18112/openneuro.ds003816.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds003816.v1.0.1},
}

About This Dataset#

This dataset contains Pre-rest ; Post-rest; Radiating LKM to Self; Radiating LKM to Other; Visualize Self and Visualize Other (eyes closed) states EEG and ECG recordings with 48 participants. Among of 48 participants, 15 participants were interested to participate as long-term practitioner. They were able to participate EEG and ECG data recording more than 10 times within two-month. For the rest of the participants recorded only once respectively. High-density EEG and one channel ECG were collected simultaneously by a bio-signal amplifier (actiCHamp, Brain Products, German) from the 48 participants during the whole LKM training session with a sampling frequency of 1000 Hz. 128 EEG electrodes were fixed on the participant’s scalp according to the International 10-20 System. One ECG electrode was fixed on V3 lead. All Electrodes’ impedance was kept under 20kohm to maintain a good signal- to-noise ratio.

Dataset Information#

Dataset ID	`DS003816`
Title	The Effect of Buddhism Derived Loving Kindness Meditation on Modulating EEG: Long-term and Short-term Effect
Year	—
Authors	SUN, Rui, Ven WONG, Goon Fui, GAO, Jungling
License	CC0
Citation / DOI	10.18112/openneuro.ds003816.v1.0.1
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: 48
Recordings: 1077
Tasks: 8

Channels & sampling rate

Channels: 128 (1069), 127 (8)
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS003816 class to access this dataset programmatically.

class eegdash.dataset.DS003816(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003816. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 48; recordings: 1077; tasks: 8.

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/ds003816 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003816

Examples

>>> from eegdash.dataset import DS003816
>>> dataset = DS003816(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