DS004256#

Encoding of Sound Source Elevation in Human Cortex

Access recordings and metadata through EEGDash.

Citation: Ole Bialas, Marc Schoenwiesner, Burkhard Maess (2022). Encoding of Sound Source Elevation in Human Cortex. 10.18112/openneuro.ds004256.v1.0.5

Modality: eeg Subjects: 53 Recordings: 584 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004256

dataset = DS004256(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 = DS004256(cache_dir="./data", subject="01")

Advanced query

dataset = DS004256(
    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{ds004256,
  title = {Encoding of Sound Source Elevation in Human Cortex},
  author = {Ole Bialas and Marc Schoenwiesner and Burkhard Maess},
  doi = {10.18112/openneuro.ds004256.v1.0.5},
  url = {https://doi.org/10.18112/openneuro.ds004256.v1.0.5},
}

About This Dataset#

Overview

The dataset consists of data from two experiments in which subjects were presented bursts of noise from loudspeakers at different elevations. Subjects who participated in either experiment were initially tested in their ability to localize elevated sound sources. Both experiments were conducted in a hemi-anechoic chamber.

Localization Tests

Bursts of pink noise were presented from loudspeakers at different elevations and 10° azimuth (to the listeners right). In the localization test preceding experiment I, these loudspeakers were positioned at elevations of +50°, +25°, 0° and -25° while the localization test preceding experiment II also included a loudspeaker at -50° elevation. Localization test data is missing for sub-001, sub-002 and sub-003

Deviant Detection (Experiment 1)

Subjects 001-023 participated in this experiment. Subjects heard a long trail of noise from one loudspeaker (adapter), followed by a short burst of noise from another loudspeaker (probe). The elevation of the adapter and probe are encoded in the event values: 2: adapter at 37.5°, probe at 12.5° 3: adapter at 37.5°, probe at -12.5° 4: adapter at 37.5°, probe at -37.5° 5: adapter at -37.5°, probe at 37.5° 6: adapter at -37.5°, probe at 12.5° 7: adapter at -37.5°, probe at -12.5° 8: no adapter, any non-target location (deviant) The behavioral data contains the trial numbers where a deviant was presented and weather the subject responded within one second by pressing a button.

One-Back (Experiment II)

Subjects 100-129 participated in this experiment. Subjects heard a long trail of white noise through open headphones followed by a short burst of noise from one of the loudspeakers. The loudspeaker’s elevation is encoded in the event values: 1: 37.5°, 2: 12.5°, 3:-23.5°, 4:-37.5° Roughly five percent of trials were targets where subjects heard a beep after the trial, prompting them to localize the previously heard sound. The number of those target trials, as well as the target’s elevation and the subject’s response can be found in thee behavioral data.

Dataset Information#

Dataset ID	`DS004256`
Title	Encoding of Sound Source Elevation in Human Cortex
Year	2022
Authors	Ole Bialas, Marc Schoenwiesner, Burkhard Maess
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004256.v1.0.5
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: 53
Recordings: 584
Tasks: 2

Channels & sampling rate

Channels: 64
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS004256 class to access this dataset programmatically.

class eegdash.dataset.DS004256(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004256. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 53; recordings: 53; 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/ds004256 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004256

Examples

>>> from eegdash.dataset import DS004256
>>> dataset = DS004256(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