DS002218#

Auditory and Visual Rhythm Omission EEG

Access recordings and metadata through EEGDash.

Citation: Daniel C Comstock, Ramesh Balasubramaniam (2019). Auditory and Visual Rhythm Omission EEG. mockDOI

Modality: eeg Subjects: 18 Recordings: 133 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS002218

dataset = DS002218(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS002218(cache_dir="./data", subject="01")

Advanced query

dataset = DS002218(
    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{ds002218,
  title = {Auditory and Visual Rhythm Omission EEG},
  author = {Daniel C Comstock and Ramesh Balasubramaniam},
  doi = {mockDOI},
  url = {https://doi.org/mockDOI},
}

About This Dataset#

This EEG dataset was recorded as part of a study of the predictive mechanisms of rhythm perception by using an omission paradigm to separate out predictive neural activity from sensory evoked neural activity. The study had 18 participants listen to auditory rhythms and watch visual flashing rhythms separately. The stimulus trains of both kinds of rhythms contained occasional omissions. Code for preprocessing, time/freq computation, frequency band extraction and statistics is provided. Cluster formation was performed using the EEGLAB Study function.

Dataset Information#

Dataset ID

DS002218

Title

Auditory and Visual Rhythm Omission EEG

Year

2019

Authors

Daniel C Comstock, Ramesh Balasubramaniam

License

CC0

Citation / DOI

mockDOI

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds002218,
  title = {Auditory and Visual Rhythm Omission EEG},
  author = {Daniel C Comstock and Ramesh Balasubramaniam},
  doi = {mockDOI},
  url = {https://doi.org/mockDOI},
}

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: 18

  • Recordings: 133

  • Tasks: 1

Channels & sampling rate

  • Channels: 32

  • Sampling rate (Hz): 256.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 1.9 GB

  • File count: 133

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: mockDOI

Provenance

API Reference#

Use the DS002218 class to access this dataset programmatically.

class eegdash.dataset.DS002218(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds002218. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 18; recordings: 18; 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/ds002218 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002218

Examples

>>> from eegdash.dataset import DS002218
>>> dataset = DS002218(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

See Also#