DS005403#

Delayed Auditory Feedback EEG/EGG

Access recordings and metadata through EEGDash.

Citation: Veillette, J., Rosen, J., Margoliash, D., Nusbaum, H. (2024). Delayed Auditory Feedback EEG/EGG. 10.18112/openneuro.ds005403.v1.0.1

Modality: eeg Subjects: 32 Recordings: 291 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005403

dataset = DS005403(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 = DS005403(cache_dir="./data", subject="01")

Advanced query

dataset = DS005403(
    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{ds005403,
  title = {Delayed Auditory Feedback EEG/EGG},
  author = {Veillette, J. and Rosen, J. and Margoliash, D. and Nusbaum, H.},
  doi = {10.18112/openneuro.ds005403.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005403.v1.0.1},
}

About This Dataset#

Notes

Electroglottography (EGG) and audio are included in the EEG files themselves, rather than in sidecar files, as they were converted from analog to digital on the same hardware. The audio is the audio the subject heard, i.e. their delayed auditory feedback. If you want the speech waveform aligned to the time the subject produced it, you can shift the audio back by the timestamps recorded (for each trial) in the delay field of the events sidecar file.

EGG has already been minimally preprocessed to correct for phase delays induced by the built-in hardware filter of the EGG amplifier by applying an equivalent software filter in the opposite temporal direction. (This is the same strategy employed by “zero phase shift” filters in MATLAB and scipy.)

Data was organized according the the BIDS standard for EEG data using the MNE-BIDS software (Appelhoff et al., 2019; Pernet et al., 2019).

References

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896

Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8

Dataset Information#

Dataset ID	`DS005403`
Title	Delayed Auditory Feedback EEG/EGG
Year	2024
Authors	Veillette, J., Rosen, J., Margoliash, D., Nusbaum, H.
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005403.v1.0.1
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: 32
Recordings: 291
Tasks: 1

Channels & sampling rate

Channels: 62 (32), 66 (32)
Sampling rate (Hz): 10000.0
Duration (hours): 0.0

API Reference#

Use the DS005403 class to access this dataset programmatically.

class eegdash.dataset.DS005403(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005403. Modality: eeg; Experiment type: Motor; Subject type: Unknown. Subjects: 32; recordings: 32; 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/ds005403 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005403

Examples

>>> from eegdash.dataset import DS005403
>>> dataset = DS005403(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