DS007558: eeg dataset, 67 subjects#

EEG Pre/Post Intervention Dataset

Access recordings and metadata through EEGDash.

Citation: Mengsha Qi (2026). EEG Pre/Post Intervention Dataset. 10.18112/openneuro.ds007558.v1.0.0

Modality: eeg Subjects: 67 Recordings: 121 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007558

dataset = DS007558(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS007558(cache_dir="./data", subject="01")

Advanced query

dataset = DS007558(
    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{ds007558,
  title = {EEG Pre/Post Intervention Dataset},
  author = {Mengsha Qi},
  doi = {10.18112/openneuro.ds007558.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007558.v1.0.0},
}

About This Dataset#

Dataset Description

Overview

This dataset contains EEG recordings from a study investigating neural activity changes before and after an intervention. The data are organized following the Brain Imaging Data Structure (BIDS) specification. The dataset includes multiple participant groups and timepoints: - Group 1, Group 2, Group 3 - Pre-intervention (pre) and Post-intervention (post)

View full README

Dataset Description

Overview

This dataset contains EEG recordings from a study investigating neural activity changes before and after an intervention. The data are organized following the Brain Imaging Data Structure (BIDS) specification. The dataset includes multiple participant groups and timepoints: - Group 1, Group 2, Group 3 - Pre-intervention (pre) and Post-intervention (post)

Participants

Participants are labeled using anonymized IDs (e.g., sub-001, sub-002, etc.). Demographic and session-related information are provided in the corresponding TSV files where applicable.

Data Structure

The dataset follows the BIDS format: - sub-XXX/

  • ses-pre/ or ses-post/ - eeg/

    • EEG recordings (.edf)

    • Metadata files (.json)

    • Events files (.tsv)

Each subject contains EEG recordings organized by session (pre/post).

Experimental Design

The study compares neural activity before and after an intervention. Participants are divided into different groups to evaluate potential differences in outcomes.

Data Acquisition

EEG data were recorded using standard acquisition systems. Detailed acquisition parameters are stored in the accompanying JSON sidecar files.

Data Processing

The dataset has been reorganized into BIDS format. File naming, metadata, and structure have been standardized to ensure compatibility with BIDS-compliant tools.

Known Issues

  • Some warnings may appear during BIDS validation but do not affect data usability.

  • All critical validation errors have been resolved.

Usage Notes

This dataset can be used for: - EEG signal analysis - Functional connectivity studies - Pre/post intervention comparisons

License

Please refer to the dataset repository for licensing information.

Acknowledgements

We thank all participants and researchers involved in data collection and processing.

Dataset Information#

Dataset ID

DS007558

Title

EEG Pre/Post Intervention Dataset

Author (year)

Qi2026

Canonical

—

Importable as

DS007558, Qi2026

Year

2026

Authors

Mengsha Qi

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds007558.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds007558,
  title = {EEG Pre/Post Intervention Dataset},
  author = {Mengsha Qi},
  doi = {10.18112/openneuro.ds007558.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007558.v1.0.0},
}

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: 67

  • Recordings: 121

  • Tasks: 1

Channels & sampling rate

  • Channels: 19 (106), 21 (13), 20 (2)

  • Sampling rate (Hz): 200.0

  • Duration (hours): 25.98491805555556

Tags

  • Pathology: Not specified

  • Modality: Resting State

  • Type: Clinical/Intervention

Files & format

  • Size on disk: 686.4 MB

  • File count: 121

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds007558.v1.0.0

Provenance

API Reference#

Use the DS007558 class to access this dataset programmatically.

class eegdash.dataset.DS007558(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

EEG Pre/Post Intervention Dataset

Study:

ds007558 (OpenNeuro)

Author (year):

Qi2026

Canonical:

—

Also importable as: DS007558, Qi2026.

Modality: eeg; Experiment type: Clinical/Intervention; Subject type: Unknown. Subjects: 67; recordings: 121; 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/ds007558 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007558 DOI: https://doi.org/10.18112/openneuro.ds007558.v1.0.0

Examples

>>> from eegdash.dataset import DS007558
>>> dataset = DS007558(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#