DS007172#

EEG-Asymmetries Dataset

Access recordings and metadata through EEGDash.

Citation: Petunia Reinke, Lisa Deneke, Sebastian Ocklenburg (2026). EEG-Asymmetries Dataset. 10.18112/openneuro.ds007172.v1.0.0

Modality: eeg Subjects: 100 Recordings: 3810 License: CC0 Source: openneuro

Metadata: Complete (100%)

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},
}

About This Dataset#

References BIDS

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

View full README

References BIDS

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

References Dataset

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

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

Dataset Information#

Dataset ID	`DS007172`
Title	EEG-Asymmetries Dataset
Year	2026
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

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: 100
Recordings: 3810
Tasks: 6

Channels & sampling rate

Channels: 32 (496), 29 (5)
Sampling rate (Hz): 500.0 (496), 1000.0 (5)
Duration (hours): 0.0

API Reference#

Use the DS007172 class to access this dataset programmatically.

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

Bases: EEGDashDataset

OpenNeuro dataset ds007172. 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

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, 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