DS007615: eeg dataset, 69 subjects#

LDAEP and resting-state EEG in healthy women

Access recordings and metadata through EEGDash.

Citation: Henrik Normannseth, Stein Andersson, Christoffer Hatlestad-Hall (2026). LDAEP and resting-state EEG in healthy women. 10.18112/openneuro.ds007615.v1.0.0

Modality: eeg Subjects: 69 Recordings: 192 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007615

dataset = DS007615(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS007615(cache_dir="./data", subject="01")

Advanced query

dataset = DS007615(
    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{ds007615,
  title = {LDAEP and resting-state EEG in healthy women},
  author = {Henrik Normannseth and Stein Andersson and Christoffer Hatlestad-Hall},
  doi = {10.18112/openneuro.ds007615.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007615.v1.0.0},
}

About This Dataset#

LDAEP and resting-state EEG in healthy women

The dataset at a glance

  • 69 participants, all female.

  • Age range: 19-40 years, mean age 25.3 years (SD 4.2).

  • A single recording session comprising two paradigms: LDAEP (54 participants) and resting-state (eyes-open and eyes-closed; 69 participants).

  • 64 EEG channels and 4 auxiliary oculogram channels.

View full README

LDAEP and resting-state EEG in healthy women

The dataset at a glance

  • 69 participants, all female.

  • Age range: 19-40 years, mean age 25.3 years (SD 4.2).

  • A single recording session comprising two paradigms: LDAEP (54 participants) and resting-state (eyes-open and eyes-closed; 69 participants).

  • 64 EEG channels and 4 auxiliary oculogram channels.

  • Signal sampling rate: 2048 Hz.

  • Additional data include hormonal contraceptive use, menstrual cycle phase, depressive symptoms (BDI-II), impulsivity (UPPS-P), and lifestyle factors.

Introduction

This dataset contains EEG recordings from 69 healthy women, acquired at the Department of Psychology, University of Oslo, Norway. The data were collected as part of a study investigating the relationship between hormonal contraceptive use and central serotonergic activity indexed by the loudness dependence of auditory evoked potentials (LDAEP). Two paradigms were recorded per participant: a resting-state recording (four minutes eyes closed followed by four minutes eyes open) and na LDAEP paradigm (1000 Hz tones at five intensity levels: 55, 65, 75, 85, and 95 dB SPL; 80 trials per level). The resting-state recording was always conducted first to avoid auditory stimulus contamination. Resting-state data are available for all 69 participants. Due to a technical issue with the auditory stimulation equipment, LDAEP data are available for 54 of the 69 participants. The data were recorded with a BioSemi ActiveTwo system, using 64 Ag-AgCl electrodes positioned according to the extended 10-20 system (10-10), at a sampling rate of 2048 Hz. Raw data are stored in BrainVision format (triplet of *.eeg, *.vhdr, *.vmrk). Alongside the EEG data, the dataset includes questionnaire data on hormonal contraceptive use, menstrual cycle, depressive symptoms (BDI-II), impulsivity (UPPS-P), and lifestyle factors.

Disclaimer

The dataset is provided “as is”. The authors take no responsibility with regard to data quality. The user is solely responsible for ascertaining that the data used for publications or in other contexts fulfil the required quality criteria.

The data

Raw data files

Each participant’s EEG data are stored in the sub-##/eeg/ directory in BrainVision format. Up to two tasks are included per participant: - task-rest: Eyes-closed (acq-ec) and eyes-open (acq-eo) resting-state (4 minutes each). Available for all 69 participants. - task-ldaep: LDAEP auditory stimulation paradigm. Available for 54 participants only.

Each task/acq combination comprises three data files (*.eeg, *.vhdr, *.vmrk) and is accompanied by a sidecar metadata file (*.json), a channels information file (*_channels.tsv), and an events file (*_events.tsv with *_events.json). The data signals are unfiltered, except for a standard software anti-aliasing filter (recorded in Norway; the line noise frequency is 50 Hz). The EOG channels – HOG1, HOG2, VOG1, VOG2 – are positioned near the outer canthi (1 = left, 2 = right) and above (1) and below (2) the right eye. Please note that the data does not come with any pre-defined quality assessment. Whilst most data files are of high quality, individual files may vary. It is the user’s responsibility to verify the quality of each data file. The dataset does not include quality assessment or preprocessing code. For an example LDAEP pipeline, please refer to the paper referenced below.

Participant and phenotype data

Core demographic variables are provided in participants.tsv at the root level (age, sex, and hormonal contraceptive user group). Additional participant-level data are organised in the phenotype/ directory: - hc_usage.tsv: Hormonal contraceptive usage details and menstrual cycle data (26 variables). Includes contraceptive type, progestin type, duration of use, self-reported mood changes and side effects (coded yes/no), prior HC use history, pregnancy history, and menstrual cycle phase. - lifestyle.tsv: Medication use, nicotine use and type, alcohol consumption, and recreational drug use and frequency (7 variables). - bdi.tsv: Beck Depression Inventory-II total score, cognitive-affective subscale score, and somatic subscale score (4 variables). Subscales follow the female-specific factor structure of Dozois et al. (1998). - upps.tsv: UPPS-P Impulsive Behavior Scale subscale scores: lack of perseverance, urgency, lack of premeditation, and sensation seeking (5 variables).

Each phenotype file is accompanied by a JSON sidecar with variable descriptions and coding schemes. To protect participant privacy, free-text questionnaire responses were excluded from the dataset; only coded categorical and numeric variables are provided.

How to cite

All use of this dataset in a publication context requires the following paper to be cited: Normannseth, H., Hatlestad-Hall, C., Rygvold, T. W., Hadzic, A., & Andersson, S. (2025). Hormonal contraceptive use is associated with reduced central serotonergic activity indexed by the loudness dependence of auditory evoked potentials. Frontiers in Human Neuroscience, 19, 1647425. https://doi.org/10.3389/fnhum.2025.1647425 A dataset descriptor article is currently in works.

Contact

Questions regarding the dataset may be addressed to the corresponding author, Christoffer Hatlestad-Hall, Department of Neurology, Oslo University Hospital, Norway (chrihat (at) ous-research.no).

References

The dataset was standardised and organised in accordance with BIDS using MNE-BIDS: Appelhoff, S., Sanderson, M., Brooks, T., Van Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A., & Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software, 4(44), 1896. https://doi.org/10.21105/joss.01896 The relevant BIDS specification publications: Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., Flandin, G., Ghosh, S. S., Glatard, T., Halchenko, Y. O., Handwerker, D. A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B. N., Nichols, T. E., Pellman, J., … Poldrack, R. A. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3(1), 160044. https://doi.org/10.1038/sdata.2016.44 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(1), 103–108. https://doi.org/10.1038/s41597-019-0104-8 Other articles: Dozois, D.J., Dobson, K.S., & Ahnberg, J.L. (1998). A psychometric evaluation of the Beck Depression inventory-II. Psychological Assessment, 10, 83-89. https://doi.org/10.1037/1040-3590.10.2.83

Dataset Information#

Dataset ID

DS007615

Title

LDAEP and resting-state EEG in healthy women

Author (year)

Normannseth2026

Canonical

—

Importable as

DS007615, Normannseth2026

Year

2026

Authors

Henrik Normannseth, Stein Andersson, Christoffer Hatlestad-Hall

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds007615.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds007615,
  title = {LDAEP and resting-state EEG in healthy women},
  author = {Henrik Normannseth and Stein Andersson and Christoffer Hatlestad-Hall},
  doi = {10.18112/openneuro.ds007615.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007615.v1.0.0},
}

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: 69

  • Recordings: 192

  • Tasks: 2

Channels & sampling rate

  • Channels: 68

  • Sampling rate (Hz): 2048.0

  • Duration (hours): 18.548532307942708

Tags

  • Pathology: Healthy

  • Modality: Auditory

  • Type: Perception

Files & format

  • Size on disk: 34.6 GB

  • File count: 192

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds007615.v1.0.0

Provenance

API Reference#

Use the DS007615 class to access this dataset programmatically.

class eegdash.dataset.DS007615(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

LDAEP and resting-state EEG in healthy women

Study:

ds007615 (OpenNeuro)

Author (year):

Normannseth2026

Canonical:

—

Also importable as: DS007615, Normannseth2026.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 69; recordings: 192; tasks: 2.

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/ds007615 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007615 DOI: https://doi.org/10.18112/openneuro.ds007615.v1.0.0

Examples

>>> from eegdash.dataset import DS007615
>>> dataset = DS007615(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

See Also#