EEGdash›OpenNeuro›DS007721

Iss. 7721 · 20 subjects · 40 recordings · CC0

Dataset Brief · Bone conducted responses using the parallel auditory brainste…

DS007721: eeg dataset, 20 subjects#

Bone conducted responses using the parallel auditory brainstem response (pABR) paradigm

Citation: Melissa J. Polonenko, Ross K. Maddox (2026). Bone conducted responses using the parallel auditory brainstem response (pABR) paradigm. 10.18112/openneuro.ds007721.v1.0.1

20-participant EEG dataset — Bone conducted responses using the parallel auditory brainstem response (pABR) paradigm.

Data & curation Melissa J. Polonenko · Ross K. Maddox
Year 2026 · Distributed via OpenNeuro
Funding National Institute on Deafness and Other Communication Disorders (NIDCD) of the National Institutes of Health: R01 DC017962

EEG · 3 ch10000 HzBIDS 1.7.02 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 DS007721

dataset = DS007721(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 = DS007721(cache_dir="./data", subject="01")

Advanced query

dataset = DS007721(
    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{ds007721,
  title = {Bone conducted responses using the parallel auditory brainstem response (pABR) paradigm},
  author = {Melissa J. Polonenko and Ross K. Maddox},
  doi = {10.18112/openneuro.ds007721.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds007721.v1.0.1},
}

§ 02Study · The README

About This Dataset#

Please contact the following author for further information:

Melissa Polonenko(email: mpolonen@umn.edu)

This is the dataset for the paper

Polonenko MJ & Maddox RK (2026), with citation listed below.

README

Details related to access to the data

Ear and Hearing:

Polonenko, MJ, & Maddox, RK. Bone conducted responses using the parallel auditory brainstem response (pABR) paradigm. Accepted March 27, 2026.

View full README

README

Details related to access to the data

Ear and Hearing:

Polonenko, MJ, & Maddox, RK. Bone conducted responses using the parallel auditory brainstem response (pABR) paradigm. Accepted March 27, 2026.

BioRxiv:

Polonenko, MJ, & Maddox, RK. Bone conducted responses using the parallel auditory brainstem response (pABR) paradigm. bioRxiv [Preprint]. 2025 Oct 7:2025.10.07.680974. doi: 10.1101/2025.10.07.680974.

Data was collected from October to November 2024. Aim: This study aimed to confirm the feasibility of bone conduction pABR. 1) Create normative values for bone conducted pABR stimuli. 2) Compare ABRs to air- and bone-conducted stimuli.

The details of the experiment can be found in the bioRxiv pre-print and article cited above.

Stimuli for EEG:
pABR stimuli with 500, 1000, 2000, 4000, 8000 Hz tone pips. AC only: dichotic presentation with 40 Hz stimulation rate BC: BC stimuli at 40 Hz presentation rate; contralateral masking presented

via AC at 400 Hz stimulation rate

Levels: 20, 40, 60, 70, 50, 30 dB peSPL 600 x 1 s trials for each level for each task (AC, BC) Total of 3,600 trials for each task x 2 tasks = 7,200 trials total Total of 60 minutes per task for a total of 120 minutes EEG recording.

Stimuli for BEH:
pABR stimuli with 500, 1000, 2000, 4000, 8000 Hz tone pips Tone pips presented separately Stimulation rates: 20, 40, 60, 80 Hz

The code for analyses is available in this dataset.

Format

The EEG dataset is formatted according to the EEG Brain Imaging Data Structure.

It includes EEG recording in raw brainvision format (.eeg, .vhdr, .vmrk) and stimuli files in format of .hdf5. The stimuli files contain the audio (‘x’) and the pulses (‘x_pulse’), the latter of which is used to create the pulse inds for the regressors [take indices where abs(x_pulse) > 0.5] in the cross- correlation.

Generally, you can find detailed event data in the .tsv files and descriptions in the accompanying .json files. Raw eeg files are provided in the Brain Products format.

The BEH dataset has a .log and .tab file with the data from the trackers that can be recontructed using the expyfun package. This data is provided on GitHub at polonenkolab/pabr_bc_poc

Participants

22 participants, mean ± SD age of 22.8 ± 2.7 years (19-31 years) Note: participants 15 & 21 do not have EEG data;

participants 3 & 4 do not have BEH data

Inclusion criteria:

Age between 18-40 years

Normal hearing: audiometric thresholds <25 dB HL from 500 to 8000 Hz

Please see participants.tsv for more information.

Apparatus

Participants sat in a darkened sound-isolating booth and rested while listening to the stimuli. They were encouraged to sleep. Stimuli were presented through ER-2 insert earphones (AC) and a B-81 vibrator (BC) plugged into an RME Digiface USB digital sound card. Custom python scripts using expyfun were used to control the experiment and stimulus presentation.

Details about the experiment

For a detailed description of the task, see Polonenko & Maddox (2026) and the supplied task-f0s_eeg.json file. The levels for each task were interleaved interleaved.

Trigger onset times are not corrected for the delay of the insert earphones, but the tsv files have both the uncorrected and corrected samples.

Triggers with values of “1” were recorded to the onset of the 1 s audio, and shortly after triggers with values of “4” or “8” were stamped to indicate the which of the runs (levels) and tokens was played. This was done by converting the decimal trial number to bits, denoted b, then calculating 2 ** (b + 2).

We’ve specified the metadata of the events in each of the ‘*_eeg_events.tsv” file, which is sufficient to know which trial corresponded to run and which stimulus file to use and which token index to use.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=20, range 19–31 yr, mean 22.8 yr)

15202530

Other · 20

Sex composition

Female
19
Male
2
Other
1
F : M ratio
9.50 : 1

86% female · n = 22 subjects with reported sex.

Channel counts: 3 ch (n=40 recordings)

Sampling frequencies: 10000.0 Hz (n=40 recordings)

Total recording duration: 42 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 3 ch · EEG · 10000 Hz · 20 subjects, 40 recordings

Live trace viewer — sub-13 · task-bc

Showing one representative recording out of 20 subjects and 40 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.

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

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.DS007721(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asDS007721

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

class eegdash.dataset.DS007721(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bone conducted responses using the parallel auditory brainstem response (pABR) paradigm

Study:: ds007721 (OpenNeuro)
Author (year):: nan
Canonical:: —

Also importable as: DS007721, nan.

Modality: eeg. Subjects: 20; recordings: 40; 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/ds007721 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007721 DOI: https://doi.org/10.18112/openneuro.ds007721.v1.0.1

Examples

>>> from eegdash.dataset import DS007721
>>> dataset = DS007721(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 — DS007721.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 ds007721 to reproduce the tutorial on this dataset.

Citation

Melissa J. Polonenko, Ross K. Maddox (2026). Bone conducted responses using the parallel auditory brainstem response (pABR) paradigm. 10.18112/openneuro.ds007721.v1.0.1

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds007721.v1.0.1.

Related & sibling datasets

DS004356EEG · 22 subj DS004561EEG · 23 subj DS003626EEG · 10 subj DS004582EEG · 73 subj DS007509EEG · 69 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.7.0

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds007721.v1.0.1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`DS007721`
Title	Bone conducted responses using the parallel auditory brainstem response (pABR) paradigm
Author (year)	—
Canonical	—
Importable as	`DS007721`
Year	2026
Authors	Melissa J. Polonenko, Ross K. Maddox
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007721.v1.0.1
Source links	OpenNeuro \| NeMAR \| Source URL

DS007721: eeg dataset, 20 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#