DS004356#

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

Access recordings and metadata through EEGDash.

Citation: Tong Shan, Madeline S. Cappelloni, Ross K. Maddox (2022). Subcortical responses to music and speech are alike while cortical responses diverge. 10.18112/openneuro.ds004356.v2.2.1

Modality: eeg Subjects: 22 Recordings: 639 License: CC0 Source: openneuro Citations: 2.0

Metadata: Complete (100%)

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},
}

About This Dataset#

README

Details related to access to the data

Please contact the following authors for further information: - Tong Shan (email: tshan@ur.rochester.edu)

View full README

README

Details related to access to the data

Please contact the following authors for further information: - Tong Shan (email: tshan@ur.rochester.edu) - Ross K. Maddox (email: rmaddox@ur.rochester.edu)

Overview

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.

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.

Dataset Information#

Dataset ID	`DS004356`
Title	Subcortical responses to music and speech are alike while cortical responses diverge
Year	2022
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

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: 22
Recordings: 639
Tasks: 1

Channels & sampling rate

Channels: 34
Sampling rate (Hz): 10000.0
Duration (hours): 0.0

API Reference#

Use the DS004356 class to access this dataset programmatically.

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

Bases: EEGDashDataset

OpenNeuro dataset ds004356. Modality: eeg; Experiment type: Perception; Subject type: Healthy. 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

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, 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