EEGdash›OpenNeuro›DS001849
Iss. 1849 · 20 subjects · 120 recordings · CC0
Dataset Brief · RS_TMSEEG_Data

DS001849: eeg dataset, 20 subjects#

RS_TMSEEG_Data

Citation: Michael Freedberg, Jack A. Reeves, Sara J. Hussain, Kareem A. Zaghloul, Eric M. Wassermann (—). RS_TMSEEG_Data. 10.18112/openneuro.ds001849.v1.0.2

20-participant EEG dataset — RS_TMSEEG_Data.

Data & curation Michael Freedberg · Jack A. Reeves · Sara J. Hussain · Kareem A. Zaghloul · Eric M. Wassermann
EEG · 30 ch5000 HzBIDS 2.7.0Task · tmseegrestHealthyMultisensoryClinical/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#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS001849

dataset = DS001849(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS001849(cache_dir="./data", subject="01")

Advanced query

dataset = DS001849(
    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{ds001849,
  title = {RS_TMSEEG_Data},
  author = {Michael Freedberg and Jack A. Reeves and Sara J. Hussain and Kareem A. Zaghloul and Eric M. Wassermann},
  doi = {10.18112/openneuro.ds001849.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds001849.v1.0.2},
}
§ 02Study · The README

About This Dataset#

No README content is available for this dataset.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 30 ch (n=120 recordings)

Sampling frequencies: 5000.0 Hz (n=120 recordings)

Total recording duration: 20 h 44 min

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 30 ch · EEG · 5000 Hz · 20 subjects, 120 recordings
Live trace viewer — sub-13 · task-tmseegrest

Showing one representative recording out of 20 subjects and 120 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 · 30 sensors — 30 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.

NEMAR pipeline statistics — DS001849
HED event descriptors word cloud HED event descriptors word cloud — DS001849
§ 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…
Full dataset metadata table

Dataset ID

DS001849

Title

RS_TMSEEG_Data

Author (year)

Freedberg2019

Canonical

—

Importable as

DS001849, Freedberg2019

Year

—

Authors

Michael Freedberg, Jack A. Reeves, Sara J. Hussain, Kareem A. Zaghloul, Eric M. Wassermann

License

CC0

Citation / DOI

10.18112/openneuro.ds001849.v1.0.2

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds001849,
  title = {RS_TMSEEG_Data},
  author = {Michael Freedberg and Jack A. Reeves and Sara J. Hussain and Kareem A. Zaghloul and Eric M. Wassermann},
  doi = {10.18112/openneuro.ds001849.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds001849.v1.0.2},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS001849(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Freedberg2019
Canonical—
Importable asDS001849 · Freedberg2019
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS001849(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

RS_TMSEEG_Data

Study:

ds001849 (OpenNeuro)

Author (year):

Freedberg2019

Canonical:

—

Also importable as: DS001849, Freedberg2019.

Modality: eeg. Subjects: 20; recordings: 120; 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/ds001849 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds001849 DOI: https://doi.org/10.18112/openneuro.ds001849.v1.0.2 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS001849
>>> dataset = DS001849(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 FacePre-bundled mirror at EEGDash/ds001849 · pull with datasets.load_dataset("EEGDash/ds001849").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS001849.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 ds001849 to reproduce the tutorial on this dataset.

Citation

Michael Freedberg, Jack A. Reeves, Sara J. Hussain, Kareem A. Zaghloul, Eric M. Wassermann (n.d.). RS_TMSEEG_Data. 10.18112/openneuro.ds001849.v1.0.2

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds001849.v1.0.2.

BIDS
BIDS 2.7.0
Sidecars
channels
Machine-readable
schema.org/Dataset · Croissant

See Also#