EEGdash›OpenNeuro›DS008037

Iss. 8037 · 123 subjects · 670 recordings · CC0

Dataset Brief · The neural dynamics of working memory and transcranial magnet…

DS008037: eeg dataset, 123 subjects#

The neural dynamics of working memory and transcranial magnetic stimulation

Access recordings and metadata through EEGDash.

Citation: Mana Biabani, Nigel C. Rogasch (2026). The neural dynamics of working memory and transcranial magnetic stimulation. 10.18112/openneuro.ds008037.v1.0.0

Modality: eeg Subjects: 123 Recordings: 670 License: CC0 Source: openneuro

Metadata: Complete (100%)

123-participant EEG dataset — The neural dynamics of working memory and transcranial magnetic stimulation.

Data & curation Mana Biabani · Nigel C. Rogasch
Senior author Nigel C. Rogasch
Year 2026 · Distributed via OpenNeuro
Funding DP170100738 · DE180100741 · + 1 more

EEG · 63 (392), 62 (278) ch1000 HzBIDS 1.11.13 tasks

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 DS008037

dataset = DS008037(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 = DS008037(cache_dir="./data", subject="01")

Advanced query

dataset = DS008037(
    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{ds008037,
  title = {The neural dynamics of working memory and transcranial magnetic stimulation},
  author = {Mana Biabani and Nigel C. Rogasch},
  doi = {10.18112/openneuro.ds008037.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds008037.v1.0.0},
}

§ 02Study · The README

About This Dataset#

The dataset includes de-identified resting-state EEG recordings (rest), working-memory EEG and behavioural data (workingmemory), TMS-EEG recordings (tmseeg) from four stimulation sites including prefrontal cortex, premotor cortex, parietal cortex, and shoulder as a control condition, as well as behavioural data from a multi-task cognitive battery comprising the bar (bar), backwards digit span (bds), colour (clt), go/no-go (gng), n-back (nback), operation span (opn), orientation (ort), symbol search (sbs), stop-signal (sst), and symmetry span (sym) tasks. Structural MRI data were collected as part of the broader study but are not included in this OpenNeuro release.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=123, range 18–46 yr, mean 28.6 yr)

15202530354045

Female · 75Male · 48

Sex composition

Female
75
Male
48
F : M ratio
1.56 : 1

61% female · n = 123 subjects with reported sex.

Channel counts (ch)

6263

Sampling frequencies: 1000.0 Hz (n=670 recordings)

Total recording duration: 115 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 63 (392), 62 (278) ch · EEG · 1000 Hz · 123 subjects, 670 recordings

Live trace viewer — sub-005 · task-tmseeg

Showing one representative recording out of 123 subjects and 670 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 · 62 sensors — 62 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.DS008037(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asDS008037

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

class eegdash.dataset.DS008037(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

The neural dynamics of working memory and transcranial magnetic stimulation

Study:: ds008037 (OpenNeuro)
Author (year):: —
Canonical:: —

Also importable as: DS008037.

Modality: eeg; Subject type: Unknown. Subjects: 123; recordings: 670; tasks: 3.

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/ds008037 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds008037 DOI: https://doi.org/10.18112/openneuro.ds008037.v1.0.0

Examples

>>> from eegdash.dataset import DS008037
>>> dataset = DS008037(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 — DS008037.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 ds008037 to reproduce the tutorial on this dataset.

Citation

Mana Biabani, Nigel C. Rogasch (2026). The neural dynamics of working memory and transcranial magnetic stimulation. 10.18112/openneuro.ds008037.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.ds008037.v1.0.0.

Related & sibling datasets

DS008014EEG · 78 subj DS008730EEG · 104 subj DS007827EEG · 99 subj DS008108EEG · 142 subj DS007823EEG · 173 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.11.1

Sidecars

events · events.json · channels · electrodes · coordsystem · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds008037.v1.0.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`DS008037`
Title	The neural dynamics of working memory and transcranial magnetic stimulation
Author (year)	—
Canonical	—
Importable as	`DS008037`
Year	2026
Authors	Mana Biabani, Nigel C. Rogasch
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds008037.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS008037: eeg dataset, 123 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#