DS002218: eeg dataset, 18 subjects#

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: 18 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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
Author (year)	`Comstock2019`
Canonical	—
Importable as	`DS002218`, `Comstock2019`
Year	2019
Authors	Daniel C Comstock, Ramesh Balasubramaniam
License	CC0
Citation / DOI	mockDOI
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: 18
Recordings: 18
Tasks: 1

Channels & sampling rate

Channels: 32
Sampling rate (Hz): 256.0
Duration (hours): 16.52023003472222

Electrode Layout#

Electrode layout — EEG · 32 sensors — 32 channels

Dataset Statistics#

Channel counts: 32 ch (n=18 recordings)

Sampling frequencies: 256.0 Hz (n=18 recordings)

Total recording duration: 16 h 31 min

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

Signal Preview#

Live trace viewer — sub-010 · task-Experiment

Showing one representative recording out of 18 subjects and 18 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.

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

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

Auditory and Visual Rhythm Omission EEG

Study:: ds002218 (OpenNeuro)
Author (year):: Comstock2019
Canonical:: —

Also importable as: DS002218, Comstock2019.

Modality: eeg. 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 DOI: https://doi.org/mockDOI NEMAR citation count: 1

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: 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)