DS004256: eeg dataset, 53 subjects#
Encoding of Sound Source Elevation in Human Cortex
Citation: Ole Bialas, Marc Schoenwiesner, Burkhard Maess (—). Encoding of Sound Source Elevation in Human Cortex. 10.18112/openneuro.ds004256.v1.0.5
53-participant EEG dataset — Encoding of Sound Source Elevation in Human Cortex.
Quickstart#
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)
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'])
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#
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.
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
Overview
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.
Cohort#
Dataset Statistics#
Age distribution by gender (n=30, range 20–31 yr, mean 24.4 yr)
Sex composition
Channel counts: 64 ch (n=53 recordings)
Sampling frequencies: 500.0 Hz (n=53 recordings)
Total recording duration: 42 h
Signal · Electrodes & live trace#
Live trace viewer — sub-021 · task-deviantdetection
Showing one representative recording out of
53 subjects and 53 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 · 64 sensors — 64 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
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 |
Encoding of Sound Source Elevation in Human Cortex |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Ole Bialas, Marc Schoenwiesner, Burkhard Maess |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004256 · Bialas2022eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004256(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Encoding of Sound Source Elevation in Human Cortex
- Study:
ds004256(OpenNeuro)- Author (year):
Bialas2022- Canonical:
—
Also importable as:
DS004256,Bialas2022.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
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/ds004256 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004256 DOI: https://doi.org/10.18112/openneuro.ds004256.v1.0.5 NEMAR citation count: 0
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: 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.pytorchdatasets.load_dataset("EEGDash/ds004256").huggingface
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 ds004256 to reproduce the tutorial on this dataset.
Citation
Ole Bialas, Marc Schoenwiesner, Burkhard Maess (n.d.). Encoding of Sound Source Elevation in Human Cortex. 10.18112/openneuro.ds004256.v1.0.5
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004256.v1.0.5.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset