EEGdash›OpenNeuro›DS004356

Iss. 4356 · 22 subjects · 24 recordings · CC0

Dataset Brief · Subcortical responses to music and speech are alike while cor…

DS004356: eeg dataset, 22 subjects#

Subcortical responses to music and speech are alike while cortical responses diverge

Citation: Tong Shan, Madeline S. Cappelloni, Ross K. Maddox (2024). Subcortical responses to music and speech are alike while cortical responses diverge. 10.18112/openneuro.ds004356.v2.2.1

22-participant EEG dataset — Subcortical responses to music and speech are alike while cortical responses diverge.

Data & curation Tong Shan · Madeline S. Cappelloni · Ross K. Maddox
Year 2024 · Distributed via OpenNeuro
Funding the Schmitt Program in Neuroscience

EEG · 34 ch10000 HzBIDS 1.7.0Task · MusicvsSpeechHealthyAuditoryPerception

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 DS004356

dataset = DS004356(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 = DS004356(cache_dir="./data", subject="01")

Advanced query

dataset = DS004356(
    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{ds004356,
  title = {Subcortical responses to music and speech are alike while cortical responses diverge},
  author = {Tong Shan and Madeline S. Cappelloni and Ross K. Maddox},
  doi = {10.18112/openneuro.ds004356.v2.2.1},
  url = {https://doi.org/10.18112/openneuro.ds004356.v2.2.1},
}

§ 02Study · The README

About This Dataset#

Please contact the following authors for further information:

Tong Shan (email: tshan@ur.rochester.edu)
Ross K. Maddox (email: rmaddox@ur.rochester.edu)

The goal of this study is to derive Auditory Brainstem Response (ABR) from continuous music and speech stimuli using deconvolution method. Data collected from Jun to Aug, 2021.

README

Details related to access to the data

The details of the experiment can be found at Shan et al. (2024). There were two phases in this experiment. For the first phase, ten trials of one-minute clicks were presented to the subjects. For the second phase, the 12 types (six genres of music and six types of speech) of 12 s stimuli clips were presented. There were 40 trials for each type with shuffled order. Between trials, there was a 0.5 s pause.

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

View full README

README

Details related to access to the data

The details of the experiment can be found at Shan et al. (2024). There were two phases in this experiment. For the first phase, ten trials of one-minute clicks were presented to the subjects. For the second phase, the 12 types (six genres of music and six types of speech) of 12 s stimuli clips were presented. There were 40 trials for each type with shuffled order. Between trials, there was a 0.5 s pause.

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

Format

This dataset is formatted according to the EEG Brain Imaging Data Structure. It includes EEG recording from subject 001 to subject 024 (excluding subject 014 and subject 021) in raw brainvision format (including .eeg, .vhdr, and .vmrk triplet) and stimuli files in format of .wav.

For some subjects (sub-03 & sub-19), there are 2 “runs” of data that the first run (run-01) only contains the click phase (phase 1), and the second run includes the data for the ABR analysis. Triggers with values of “1” were recorded to the onset of the stimulus, and shortly after triggers with values of “4” or “8” were stamped to indicate the stimulus types and the trial number out of 40. This was done by converting the decimal trial number to bits, denoted b, then calculating 2 ** (b + 2). Triggers of “999” denote the start of a new segment of EEG. 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.

Subjects

24 subjects participated in this study. Subject inclusion criteria 1. Age between 18-40. 2. Normal hearing: audiometric thresholds of 20 dB HL or better from 500 to 8000 Hz. 3. Speak English as their primary language. 4. Self-reported normal or correctable to normal vision.

Subject exclusion criteria 1. Subject 014 self-withdrew partway through the experiment. 2. Subject 021 was excluded because of technical problems during data collection that led to unusable data.

Therefore, after excluding the two subjects, there were 22 subjects (11 male and 11 female) with an age of 22.7 ± 5.1 (mean ± SD) years that we included in the analysis. Please see subjects.tsv for more demography.

Apparatus

Subjects were seated in a sound-isolating booth on a chair in front of a 24-inch BenQ monitor with a viewing distance of approximately 60 cm. Stimuli were presented at an average level of 65 dB SPL and a sampling rate of 48000 Hz through ER-2 insert earphones plugged into an RME Babyface Pro digital sound card. The stimulus presentation for the experiment was controlled by a python script using a custom package, expyfun.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=22, range 19–37 yr, mean 22.7 yr)

1520253035

Other · 22

Sex composition

Female
11
Male
11
F : M ratio
1.00 : 1

50% female · n = 22 subjects with reported sex.

Channel counts: 34 ch (n=24 recordings)

Sampling frequencies: 10000.0 Hz (n=24 recordings)

Total recording duration: 46 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 34 ch · EEG · 10000 Hz · 22 subjects, 24 recordings

Live trace viewer — sub-13 · task-MusicvsSpeech

Showing one representative recording out of 22 subjects and 24 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.

Electrode layout — EEG · 32 sensors — 32 channels

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.DS004356(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Shan2022

Canonical—

Importable asDS004356 · Shan2022

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

class eegdash.dataset.DS004356(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Subcortical responses to music and speech are alike while cortical responses diverge

Study:: ds004356 (OpenNeuro)
Author (year):: Shan2022
Canonical:: —

Also importable as: DS004356, Shan2022.

Modality: eeg. Subjects: 22; recordings: 24; 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/ds004356 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004356 DOI: https://doi.org/10.18112/openneuro.ds004356.v2.2.1 NEMAR citation count: 2

Examples

>>> from eegdash.dataset import DS004356
>>> dataset = DS004356(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/ds004356 · pull with datasets.load_dataset("EEGDash/ds004356").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS004356.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 ds004356 to reproduce the tutorial on this dataset.

Citation

Tong Shan, Madeline S. Cappelloni, Ross K. Maddox (2024). Subcortical responses to music and speech are alike while cortical responses diverge. 10.18112/openneuro.ds004356.v2.2.1

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds004356.v2.2.1.

Related & sibling datasets

DS004561EEG · 23 subj DS007721EEG · 20 subj DS003626EEG · 10 subj DS004582EEG · 73 subj DS007509EEG · 69 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.7.0

Sidecars

events · channels

Provenance

CC0 · 10.18112/openneuro.ds004356.v2.2.1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS004356`
Title	Subcortical responses to music and speech are alike while cortical responses diverge
Author (year)	`Shan2022`
Canonical	—
Importable as	`DS004356`, `Shan2022`
Year	2024
Authors	Tong Shan, Madeline S. Cappelloni, Ross K. Maddox
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004356.v2.2.1
Source links	OpenNeuro \| NeMAR \| Source URL

DS004356: eeg dataset, 22 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#