EEGdash›OpenNeuro›DS007558

Iss. 7558 · 67 subjects · 121 recordings · CC0

Dataset Brief · EEG Pre/Post Intervention Dataset

DS007558: eeg dataset, 67 subjects#

EEG Pre/Post Intervention Dataset

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

67-participant EEG dataset — EEG Pre/Post Intervention Dataset.

Data & curation Mengsha Qi

EEG · 19 (106), 21 (13), 20 (2) ch200 HzBIDS 1.8.0Task · rest2 sessionsResting StateClinical/Intervention

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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},
}

§ 02Study · The README

About This Dataset#

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)
Dataset Description

Overview

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
View full README
Dataset Description

Overview

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.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts (ch)

192021

Sampling frequencies: 200.0 Hz (n=121 recordings)

Total recording duration: 25 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 19 (106), 21 (13), 20 (2) ch · EEG · 200 Hz · 67 subjects, 121 recordings

Live trace viewer — sub-021 · ses-pre · task-rest

Showing one representative recording out of 67 subjects and 121 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.

Electrode layout — EEG · 19 sensors — 19 channels

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

§ 05Manifest · BIDS tree

Manifest#

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.DS007558(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Qi2026

Canonical—

Importable asDS007558 · Qi2026

Sourceeegdash/dataset/registry.py · [source ↗]

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

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: 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)

Parameters:

path (str) –

Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS007558.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.

Load one EEG recordingResolve a single record to an MNE Raw with channels and events.

EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.

Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.

Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.

Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for ds007558 to reproduce the tutorial on this dataset.

Citation

Mengsha Qi (n.d.). EEG Pre/Post Intervention Dataset. 10.18112/openneuro.ds007558.v1.0.0

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds007558.v1.0.0.

Related & sibling datasets

DS007020EEG · 94 subj DS007427EEG · 44 subj DS007347EEG · 5 subj DS006923EEG · 140 subj DS006921EEG · 38 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.8.0

Sidecars

events · channels · electrodes · coordsystem · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds007558.v1.0.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`DS007558`
Title	EEG Pre/Post Intervention Dataset
Author (year)	`Qi2026`
Canonical	—
Importable as	`DS007558`, `Qi2026`
Year	—
Authors	Mengsha Qi
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007558.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS007558: eeg dataset, 67 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#