DS008065: eeg dataset, 62 subjects#
Optimizing parameters for multiband ABRs to continuous speech
Access recordings and metadata through EEGDash.
Citation: Melissa J. Polonenko, Benjamin R. Eisenreich (2026). Optimizing parameters for multiband ABRs to continuous speech. 10.18112/openneuro.ds008065.v1.0.1
Modality: eeg Subjects: 62 Recordings: 65 License: CC0 Source: openneuro
Metadata: Complete (100%)
62-participant EEG dataset — Optimizing parameters for multiband ABRs to continuous speech.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS008065
dataset = DS008065(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS008065(cache_dir="./data", subject="01")
Advanced query
dataset = DS008065(
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{ds008065,
title = {Optimizing parameters for multiband ABRs to continuous speech},
author = {Melissa J. Polonenko and Benjamin R. Eisenreich},
doi = {10.18112/openneuro.ds008065.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds008065.v1.0.1},
}
About This Dataset#
- Please contact the following author for further information:
Melissa Polonenko(email: mpolonen@umn.edu)
This is the dataset for the paper: “Optimal parameters for measuring multiband auditory brainstem responses to continuous speech”
Polonenko MJ & Eisenreich BR (2026), with citation listed below.
README
Details related to access to the data
Trends in Hearing: Melissa J. Polonenko, Benjamin R. Eisenreich, Trends in Hearing, 2026; https://doi.org/10.1177/23312165261465722 BioRxiv: Melissa J. Polonenko, Benjamin R. Eisenreich, bioRxiv 2025.12.24.696406; doi: https://doi.org/10.64898/2025.12.24.696406
Exp1_phases
View full README
README
Details related to access to the data
Trends in Hearing: Melissa J. Polonenko, Benjamin R. Eisenreich, Trends in Hearing, 2026; https://doi.org/10.1177/23312165261465722 BioRxiv: Melissa J. Polonenko, Benjamin R. Eisenreich, bioRxiv 2025.12.24.696406; doi: https://doi.org/10.64898/2025.12.24.696406
Exp1_phases
This is the “phases” dataset for the paper Multiband auditory brainstem responses (ABRs) were derived to continuous peaky speech from one narrator with his original fundamental frequency (125 Hz).
Data was collected from October to December 2022. Aim: Determine if peaky speech made with chirp-phase profile evokes larger ABRs than zero-phase profile.
- Based on:
previous work of click vs chirp stimuli for ABRs.
- computational modeling with 11 stories with a range of natural f0s to
zero- and CE-chirp phase profiles.
The details of the experiment can be found in the bioRxiv pre-print and article cited above.
- Stimuli:
multiband peaky speech (using audiological bands, dichotic) for 1 talker (f0 = 118 Hz) at the natural f0, with zero- or chirp-phase profile. 360 x 10 s trials each of the 2 phase profiles for a total of 720 trials (120 minutes or 2 hours, 60 min each)
- The code for analyses is available in the code/exp1_phases subfolder here, and on Github:
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_play_band_zero’ and ‘x_play_band_chirp’), and regressors for the deconvolution (‘pulse_inds’ are the pulse indices for the multiband ABRs, ‘fake_pulse_inds’ are the pulse indices for creating the “common component” that is subtracted to reveal the frequency-specific ABRs).
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
17 participants, mean ± SD age of 20.4 ± 1.3 years (19-23 years) NOTE: do NOT use subjects 1 (no responses) and 13 (last 1 hr of data not saved) Thus, use 15 participant data.
- Inclusion criteria:
Age between 18-40 years
Normal hearing: audiometric thresholds <25 dB HL from 500 to 8000 Hz
Speak English as their primary language
Please see participants.tsv for more information.
Apparatus
Participants sat in a darkened sound-isolating booth and rested while listening to the audiobooks, although they were not required to pay attention to the stories.
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 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 & Eisenreich (2026) and the
supplied task-phases_eeg.json file. The 2 phase conditions were randomly
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 10 s audio, and shortly after triggers with values of “4” or “8” were stamped to indicate the which of the 2 conditions was played, the overall trial number, and the stimulus file number. 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 and which file - e.g., stimuli/exp1_phases/wizardofoz/wizardofoz_original_0001.hdf5.
Exp2_f0s
This is the “f0s” dataset for the paper Multiband auditory brainstem responses (ABRs) were derived to continuous peaky speech from two narrators (stories) with different fundamental frequencies (f0s) that were also shifted down to an average f0 of 100 Hz. Data was collected from January to April 2023.
Aim: Evaluate the f0 effect for measured multiband ABRs, using 2 talkers with different natural f0s (one <170 Hz and one >170 Hz) and both shifted down to an average f0 of 100 Hz. Based on:
1) previous work (Polonenko & Maddox, 2021, 2024) that showed for broadband ABRs, narrators with lower f0s produce larger ABRs, with an effect similar to the stimulation rate effect in click ABRs. 2) computational modeling with systematic shifting of 11 stories with a range of natural f0s to each mean f0 from 80 to 130 Hz in 10 dB steps.
The details of the experiment can be found in the bioRxiv pre-print and article cited above.
- Stimuli:
multiband peaky speech (using audiological bands, dichotic) for two talkers (f0 = 118 Hz and 180 Hz) at their natural f0s and shifted to 100 Hz, 210 x 10 s trials each of the 4 narrator-f0 combo for a total of 840 trials (2 hours 20 minutes, 35 min each) NOTE: f0s used: original f0s (118 and 180 Hz) and f0s shifted to 100 Hz
- The code for analyses is available in code/exp2_f0s subfolder herein and on Github:
Format
The dataset is formatted according to the EEG Brain Imaging Data Structure. It includes EEG recording from participant 01 to 30 in raw brainvision format (3 files: .eeg, .vhdr, .vmrk) and stimuli files in format of .hdf5. The stimuli files contain the audio (‘x_play_band_chirp_CE’), and regressors for the deconvolution (‘pulse_inds’ are the pulse indices for the multiband ABRs,
‘fake_pulse_inds’ are the pulse indices for creating the “common component” that is subtracted to reveal the frequency- specific ABRs).
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
30 participants, mean ± SD age of 20.3 ± 2.0 years (18-29 years) NOTE: removed participants sub-exp223 and sub-exp230 from analysis due to poor responses (there were problems noted during the recording). Participants sub-exp206, sub-exp208, sub-exp217 were also removed from the paper’s analysis due to noisy responses. Thus n = 25 total used in the final analysis.
- Inclusion criteria:
Age between 18-40 years
Normal hearing: audiometric thresholds 20 dB HL or better from 500 to 8000 Hz
Speak English as their primary language
Please see participants.tsv for more information.
Apparatus
Participants sat in a darkened sound-isolating booth and rested while listening to the audiobooks, although they were not required to pay attention to the stories.
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 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 & Eisenreich (2026) and the
supplied task-f0s_eeg.json file. The 4 conditions (2 narrators x 2 f0s) were
randomly interleaved for each block of trials (i.e., for trial 1, the 4
conditions were randomized).
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 10 s audio, and shortly after triggers with values of “4” or “8” were stamped to indicate the which of the 4 conditions was played, the overall trial number, and the stimulus file number. 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 (toto or wizardofoz story), which f0 (100 Hz or original f0), and which file - e.g., stimuli/exp2_f0s/toto_shifted/toto_shifted_0001.hdf5 for the toto story with 100 Hz f0.
Exp3_f0s2
This is the “f0s2” dataset for the paper Multiband auditory brainstem responses (ABRs) were derived to continuous peaky speech from two narrators (stories) with fundamental frequencies (f0s) that were shifted down to an average f0 of 90 Hz. Data was collected in June 2023.
Aim: Determine if the f0 shifted down to 90 Hz gives good ABRs across stories. Based on:
1) previous work (Polonenko & Maddox, 2021, 2024) that showed for broadband ABRs, narrators with lower f0s produce larger ABRs, with an effect similar to the stimulation rate effect in click ABRs. 2) computational modeling with systematic shifting of 11 stories with a range of natural f0s to each mean f0 from 80 to 130 Hz in 10 dB steps. 3) f0s study that showed stories shifted to 100 Hz gave larger ABRs.
The details of the experiment can be found in the bioRxiv pre-print and article cited above.
- Stimuli:
multiband peaky speech (using audiological bands, dichotic) for 3 talkers (f0s = 118, 132, 155 Hz) at their natural f0s and shifted to 90 Hz, 210 x 10 s trials each of the 3 narrators for a total of 630 trials (105 minutes or 1.75 hours, 35 min each)
- The code for analyses is available in the code/exp3_f0s2 subfolder herein and on Github:
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_play_band_chirp_CE’), and regressors for the deconvolution (‘pulse_inds’ are the pulse indices for the multiband ABRs,
‘fake_pulse_inds’ are the pulse indices for creating the “common component” that is subtracted to reveal the frequency- specific ABRs).
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 27.7 ± 10.2 years (19-57 years) NOTE: all participants included in final analysis.
- Inclusion criteria:
Age between 18-60 years
Normal hearing: audiometric thresholds 20 dB HL or better from 500 to 8000 Hz
Speak English as their primary language
Please see participants.tsv for more information.
Apparatus
Participants sat in a darkened sound-isolating booth and rested while listening to the audiobooks, although they were not required to pay attention to the stories.
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 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 & Eisenreich (2026) and the
supplied task-f0s2_eeg.json file. The 3 conditions (3 narrators) were randomly
interleaved for each block of trials (i.e., for trial 1, 3 stories randomized).
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 10 s audio, and shortly after triggers with values of “4” or “8” were stamped to indicate the which of the 3 conditions was played, the overall trial number, and the stimulus file number. 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 and which file - e.g., stimuli/exp3_f0s2/wizard/wizard_shifted_0001.hdf5.
Cohort#
Dataset Statistics#
Age distribution by gender (n=62, range 18–57 yr, mean 21.6 yr)
Sex composition
Channel counts: 3 ch (n=65 recordings)
Sampling frequencies: 10000.0 Hz (n=65 recordings)
Total recording duration: 132 h
Signal · Electrodes & live trace#
Live trace viewer — sub-exp219 · task-f0s · run-01
Showing one representative recording out of
62 subjects and 65 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
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.
Full dataset metadata table
Dataset ID |
|
Title |
Optimizing parameters for multiband ABRs to continuous speech |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
2026 |
Authors |
Melissa J. Polonenko, Benjamin R. Eisenreich |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds008065,
title = {Optimizing parameters for multiband ABRs to continuous speech},
author = {Melissa J. Polonenko and Benjamin R. Eisenreich},
doi = {10.18112/openneuro.ds008065.v1.0.1},
url = {https://doi.org/10.18112/openneuro.ds008065.v1.0.1},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.DS008065(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Optimizing parameters for multiband ABRs to continuous speech
- Study:
ds008065(OpenNeuro)- Author (year):
—
- Canonical:
—
Also importable as:
DS008065.Modality:
eeg; Subject type:Unknown. Subjects: 62; recordings: 65; tasks: 3.- 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.References
OpenNeuro dataset: https://openneuro.org/datasets/ds008065 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds008065 DOI: https://doi.org/10.18112/openneuro.ds008065.v1.0.1
Examples
>>> from eegdash.dataset import DS008065 >>> dataset = DS008065(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.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorch
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 ds008065 to reproduce the tutorial on this dataset.
Citation
Melissa J. Polonenko, Benjamin R. Eisenreich (2026). Optimizing parameters for multiband ABRs to continuous speech. 10.18112/openneuro.ds008065.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.ds008065.v1.0.1.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset