EEGdash›OpenNeuro›DS007172

Iss. 7172 · 100 subjects · 501 recordings · CC0

Dataset Brief · EEG-Asymmetries Dataset

DS007172: eeg dataset, 100 subjects#

EEG-Asymmetries Dataset

Citation: Petunia Reinke, Lisa Deneke, Sebastian Ocklenburg (2019). EEG-Asymmetries Dataset. 10.18112/openneuro.ds007172.v1.0.0

100-participant EEG dataset — EEG-Asymmetries Dataset.

Data & curation Petunia Reinke · Lisa Deneke · Sebastian Ocklenburg
Year 2019 · Distributed via OpenNeuro

EEG · 32 (496), 29 (5) ch500 Hz · mixedBIDS 1.7.06 tasksHealthyVisualAttention

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 DS007172

dataset = DS007172(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 = DS007172(cache_dir="./data", subject="01")

Advanced query

dataset = DS007172(
    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{ds007172,
  title = {EEG-Asymmetries Dataset},
  author = {Petunia Reinke and Lisa Deneke and Sebastian Ocklenburg},
  doi = {10.18112/openneuro.ds007172.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007172.v1.0.0},
}

§ 02Study · The README

About This Dataset#

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896).https://doi.org/10.21105/joss.01896

Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103.https://doi.org/10.1038/s41597-019-0104-8

Reinke, P., Deneke, L., & Ocklenburg, S. (2025). Hemispheric asymmetries in the EEG: Is there an association between N1 lateralization and alpha asymmetry?. Laterality, 1–50. Advance online publication. https://doi.org/10.1080/1357650X.2025.2591660

References BIDS

Dataset description

The dataset comprises 100 participants (53 females, 46 males, 1 diverse individual). 27 of the females were right-handed, the rest was non-right-hand dominant. Of the males 24 were right-handed, while the rest was non-right-hand dominant. The mean age of the participants was 25.6 [4.91SD] years. All participants reported normal or 2 corrected-to-normal vision, had no unilateral sensory or motor deficits, no history of mental illnesses 3 or neurologic disorders, and were currently not taking any medication.

All participants started with a resting state (RS) of approximately eight minutes, where periods of open and closed eyes were included (each period was 63seconds, leading to 4.2 minutes of open eyes and 4.2 minutes of closed eyes resting state). After the RS each participant completed four tasks in a randomized order. Each task was constructed in the same way: The participants were instructed verbally as well as in written form directly before each trial 4 began. They were told to only react to the target stimuli (animal names, female faces, and houses with pitched roofs) via a press on the space bar. Each trial consisted of three blocks: one short practice block, one block where answers should be given with the right hand, and one block where answers should be given with the left hand. The starting hand was randomized across participants. During the trials, words (words task) or pictures (faces, emotions, and houses task) were shown in the center of the screen for one second, followed by a fixation cross for 500-700ms. After 80 stimuli, the response hand was changed, leading to a total of 160 stimuli presentations for each task. For more specific information look here: Reinke, P., Deneke, L., & Ocklenburg, S. (2025). Hemispheric asymmetries in the EEG: Is there an association between N1 lateralization and alpha asymmetry?. Laterality, 1–50. Advance online publication. https://doi.org/10.1080/1357650X.2025.2591660

Trigger description

View full README

References BIDS

Dataset description

The dataset comprises 100 participants (53 females, 46 males, 1 diverse individual). 27 of the females were right-handed, the rest was non-right-hand dominant. Of the males 24 were right-handed, while the rest was non-right-hand dominant. The mean age of the participants was 25.6 [4.91SD] years. All participants reported normal or 2 corrected-to-normal vision, had no unilateral sensory or motor deficits, no history of mental illnesses 3 or neurologic disorders, and were currently not taking any medication.

All participants started with a resting state (RS) of approximately eight minutes, where periods of open and closed eyes were included (each period was 63seconds, leading to 4.2 minutes of open eyes and 4.2 minutes of closed eyes resting state). After the RS each participant completed four tasks in a randomized order. Each task was constructed in the same way: The participants were instructed verbally as well as in written form directly before each trial 4 began. They were told to only react to the target stimuli (animal names, female faces, and houses with pitched roofs) via a press on the space bar. Each trial consisted of three blocks: one short practice block, one block where answers should be given with the right hand, and one block where answers should be given with the left hand. The starting hand was randomized across participants. During the trials, words (words task) or pictures (faces, emotions, and houses task) were shown in the center of the screen for one second, followed by a fixation cross for 500-700ms. After 80 stimuli, the response hand was changed, leading to a total of 160 stimuli presentations for each task. For more specific information look here: Reinke, P., Deneke, L., & Ocklenburg, S. (2025). Hemispheric asymmetries in the EEG: Is there an association between N1 lateralization and alpha asymmetry?. Laterality, 1–50. Advance online publication. https://doi.org/10.1080/1357650X.2025.2591660

Trigger description

Resting State (“rest”):

Rest/Open: 1 Rest/Closed: 2 Words Task (“words”):

Right Hand & animal name: 13 Right Hand & non-animal word: 14 Left Hand & animal name: 23 Left Hand & non-animal word: 24 Faces Task (“faces”):

Right Hand – Male – Black: 117 Right Hand – Male – White: 118 Right Hand – Female – Black: 127 Right Hand – Female – White: 128 Left Hand – Male – Black: 217 Left Hand – Male – White: 218 Left Hand – Female – Black: 227 Left Hand – Female – White: 228 Emotions Task (“emotions”):

Right Hand – Male – Angry: 111 Right Hand – Male – Fearful: 112 Right Hand – Male – Happy (mouth open): 113 Right Hand – Male – Happy (mouth closed): 114 Right Hand – Female – Angry: 121 Right Hand – Female – Fearful: 122 Right Hand – Female – Happy (mouth open): 123 Right Hand – Female – Happy (mouth closed): 124´ Left Hand – Male – Angry: 211 Left Hand – Male – Fearful: 212 Left Hand – Male – Happy (mouth open): 213 Left Hand – Male – Happy (mouth closed): 214 Left Hand – Female – Angry: 221 Left Hand – Female – Fearful: 222 Left Hand – Female – Happy (mouth open): 223 Left Hand – Female – Happy (mouth closed): 224 Houses task (“houses”):

Right Hand & Pitched Roof: 11 Right Hand & Flat Roof: 12 Left Hand & Pitched Roof: 21 Left Hand & Flat Roof: 22

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Sex composition

Female
53
Male
46
Other
1
F : M ratio
1.15 : 1

53% female · n = 100 subjects with reported sex.

HandednessRight · 52Left · 48

Channel counts (ch)

2932

Sampling frequencies (Hz)

5001000

Total recording duration: 50 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 32 (496), 29 (5) ch · EEG · 500 Hz · mixed · 100 subjects, 501 recordings

Live trace viewer — sub-021 · task-rest

Showing one representative recording out of 100 subjects and 501 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 · 32 sensors — 32 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.DS007172(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Reinke2026

Canonical—

Importable asDS007172 · Reinke2026

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

class eegdash.dataset.DS007172(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

EEG-Asymmetries Dataset

Study:: ds007172 (OpenNeuro)
Author (year):: Reinke2026
Canonical:: —

Also importable as: DS007172, Reinke2026.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 100; recordings: 501; tasks: 6.

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/ds007172 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007172 DOI: https://doi.org/10.18112/openneuro.ds007172.v1.0.0

Examples

>>> from eegdash.dataset import DS007172
>>> dataset = DS007172(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 — DS007172.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 ds007172 to reproduce the tutorial on this dataset.

Citation

Petunia Reinke, Lisa Deneke, Sebastian Ocklenburg (2019). EEG-Asymmetries Dataset. 10.18112/openneuro.ds007172.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.ds007172.v1.0.0.

Related & sibling datasets

DS005866EEG · 60 subj DS005868EEG · 48 subj DS004120EEG · 109 subj DS003190EEG · 19 subj DS004519EEG · 40 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.7.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds007172.v1.0.0

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`DS007172`
Title	EEG-Asymmetries Dataset
Author (year)	`Reinke2026`
Canonical	—
Importable as	`DS007172`, `Reinke2026`
Year	2019
Authors	Petunia Reinke, Lisa Deneke, Sebastian Ocklenburg
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007172.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

DS007172: eeg dataset, 100 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#