EEGdash›OpenNeuro›DS005340
Iss. 5340 · 15 subjects · 15 recordings · CC0
Dataset Brief · Fundamental frequency predominantly drives talker differences…

DS005340: eeg dataset, 15 subjects#

Fundamental frequency predominantly drives talker differences in auditory brainstem responses to continuous speech

Citation: Melissa J. Polonenko, Ross K. Maddox (2024). Fundamental frequency predominantly drives talker differences in auditory brainstem responses to continuous speech. 10.18112/openneuro.ds005340.v1.0.4

15-participant EEG dataset — Fundamental frequency predominantly drives talker differences in auditory brainstem responses to continuous speech.

Data & curation Melissa J. Polonenko · Ross K. Maddox
Year 2024 · Distributed via OpenNeuro
Funding NIDCD R01DC017962
EEG · 2 ch10000 HzBIDS 1.7.0Task · peakypitchHealthyAuditoryPerception
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#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005340

dataset = DS005340(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS005340(cache_dir="./data", subject="01")

Advanced query

dataset = DS005340(
    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{ds005340,
  title = {Fundamental frequency predominantly drives talker differences in auditory brainstem responses to continuous speech},
  author = {Melissa J. Polonenko and Ross K. Maddox},
  doi = {10.18112/openneuro.ds005340.v1.0.4},
  url = {https://doi.org/10.18112/openneuro.ds005340.v1.0.4},
}
§ 02Study · The README

About This Dataset#

Please contact the following authors for further information:

Melissa Polonenko(email: mpolonen@umn.edu) Ross Maddox (email: rkmaddox@med.umich.edu)

This is the “peaky_pitchshift”” dataset for the paper

Polonenko MJ & Maddox RK (2024), with citation listed below.

README

Details related to access to the data

Peer-reviewed manuscript:

Melissa J. Polonenko, Ross K. Maddox; Fundamental frequency predominantly drives talker differences in auditory brainstem responses to continuous speech. JASA Express Lett. 1 November 2024; 4 (11): 114401. https://doi.org/10.1121/10.0034329 BioRxiv pre-print:

View full README

README

Details related to access to the data

Peer-reviewed manuscript:

Melissa J. Polonenko, Ross K. Maddox; Fundamental frequency predominantly drives talker differences in auditory brainstem responses to continuous speech. JASA Express Lett. 1 November 2024; 4 (11): 114401. https://doi.org/10.1121/10.0034329 BioRxiv pre-print:

Melissa Jane Polonenko, Ross K Maddox (2024). Fundamental frequency predominantly drives talker differences in auditory brainstem responses to continuous speech. bioRxiv 2024.07.12.603125; doi: https://doi.org/10.1101/2024.07.12.603125 Auditory brainstem responses (ABRs) were derived to continuous peaky speech from two talkers with different fundamental frequencies (f0s) and from clicks that have mean stimulus rates set to the mean f0s. Data was collected from May to June 2021.

Aims:

  1. replicate the male/female talker effect with each at their natural f0

  2. systematically determine if f0 is the main driver of this talker difference

  3. evaluate if the f0 effect resembles the click rate effect

The details of the experiment can be found at Polonenko & Maddox (2024).

Stimuli:

1) randomized click trains at 3 stimulus rates (123, 150, 183 Hz), 30 x 10 s trials each for a total of 90 trials (15 min, 5 min each rate) 2) peaky speech for a male and female narrator at 3 f0s (123, 150, 183 Hz), 120 x 10 s trials each of the 6 narrator-f0 combo for a total of 720 trials (2 hours, 20 min each) NOTE: f0s used: original f0s (low & high respectively) and f0s shifted to the other narrator’s f0 and an f0 at the midpoint between the f0s. click rates used: set to the mean f0s used for the speech

The code for stimulus preprocessing and EEG analysis is available on Github:

polonenkolab/peaky_pitchshift

Format

The dataset is formatted according to the EEG Brain Imaging Data Structure. It includes EEG recording from participant 01 to 15 in raw brainvision format (3 files: .eeg, .vhdr, .vmrk) and stimuli files in format of .hdf5. The stimuli files contain the audio (‘x’), and regressors for the deconvolution (‘pinds’ are the pulse indices, ‘anm’ is an auditory nerve model regressor,

which was used during analyses but was not included as part of the article).

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.

Participants

15 participants, mean ± SD age of 24.1 ± 6.1 years (19-35 years) Inclusion criteria:

  1. Age between 18-40 years

  2. Normal hearing: audiometric thresholds 20 dB HL or better from 500 to 8000 Hz

  3. Speak English as their primary language

Please see participants.tsv for more information.

Apparatus

Participants sat in a darkened sound-isolating booth and rested or watched silent videos with closed captioning. Stimuli were presented at an average level of 65 dB SPL and a sampling rate of 48 kHz through ER-2 insert earphones plugged into an RME Babyface Pro 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 (2024) and the supplied task-peaky_pitch_eeg.json file. The 6 peaky speech conditions (2 narrators x 3 f0s) were randomly interleaved for each block of trials (i.e., for trial 1, the 6 conditions were randomized) and the story token was randomized. This means that the participant would not be able to follow the story. For clicks the trials were not randomized (already random clicks).

Trigger onset times in the tsv files have already been corrected for the tubing delay of the insert earphones (but not in the events of the raw files).

Triggers with values of “1” were recorded to the onset of the 10 s audio, and shortly after triggers with values of “4” or “8” were stamped to indicate the overall trial number out of 120 for each speech conditon and out of 30 for each click condition. This was done by converting the decimal trial number to bits, denoted b, then calculating 2 ** (b + 2). We’ve specified these trial numbers and more metadata of the events in each of the ‘*_eeg_events.tsv” file, which is sufficient to know which trial corresponded to which type of stimulus (clicks, male narrator, female narrator), which f0 (low, mid, high), and which file - e.g., male_low_000_regress.hdf5 for the male narrator with the low f0.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=15, range 19–35 yr, mean 24.1 yr)

1520253035
Other · 15

Sex composition

15
subjects
Female
10
Male
5
F : M ratio
2.00 : 1
67% female · n = 15 subjects with reported sex.

Channel counts: 2 ch (n=15 recordings)

Sampling frequencies: 10000.0 Hz (n=15 recordings)

Total recording duration: 35 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 2 ch · EEG · 10000 Hz · 15 subjects, 15 recordings
Live trace viewer — sub-13 · task-peakypitch

Showing one representative recording out of 15 subjects and 15 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.

NEMAR pipeline statistics — DS005340
HED event descriptors word cloud HED event descriptors word cloud — DS005340
§ 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…
Full dataset metadata table

Dataset ID

DS005340

Title

Fundamental frequency predominantly drives talker differences in auditory brainstem responses to continuous speech

Author (year)

Polonenko2024_Fundamental

Canonical

—

Importable as

DS005340, Polonenko2024_Fundamental

Year

2024

Authors

Melissa J. Polonenko, Ross K. Maddox

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds005340.v1.0.4

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds005340,
  title = {Fundamental frequency predominantly drives talker differences in auditory brainstem responses to continuous speech},
  author = {Melissa J. Polonenko and Ross K. Maddox},
  doi = {10.18112/openneuro.ds005340.v1.0.4},
  url = {https://doi.org/10.18112/openneuro.ds005340.v1.0.4},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS005340(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Polonenko2024_Fundamental
Canonical—
Importable asDS005340 · Polonenko2024_Fundamental
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS005340(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Fundamental frequency predominantly drives talker differences in auditory brainstem responses to continuous speech

Study:

ds005340 (OpenNeuro)

Author (year):

Polonenko2024_Fundamental

Canonical:

—

Also importable as: DS005340, Polonenko2024_Fundamental.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 15; recordings: 15; tasks: 1.

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/ds005340 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005340 DOI: https://doi.org/10.18112/openneuro.ds005340.v1.0.4 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005340
>>> dataset = DS005340(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 FacePre-bundled mirror at EEGDash/ds005340 · pull with datasets.load_dataset("EEGDash/ds005340").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS005340.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 ds005340 to reproduce the tutorial on this dataset.

Citation

Melissa J. Polonenko, Ross K. Maddox (2024). Fundamental frequency predominantly drives talker differences in auditory brainstem responses to continuous speech. 10.18112/openneuro.ds005340.v1.0.4

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds005340.v1.0.4.

BIDS
BIDS 1.7.0
Sidecars
events · channels · eeg.json
Machine-readable
schema.org/Dataset · Croissant

See Also#