DS002578#

Visual Oddball Task (256 channels)

Access recordings and metadata through EEGDash.

Citation: Arnaud Delorme, Scott Makeig (2020). Visual Oddball Task (256 channels). 10.18112/openneuro.ds002578.v1.1.0

Modality: eeg Subjects: 2 Recordings: 22 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS002578

dataset = DS002578(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS002578(cache_dir="./data", subject="01")

Advanced query

dataset = DS002578(
    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{ds002578,
  title = {Visual Oddball Task (256 channels)},
  author = {Arnaud Delorme and Scott Makeig},
  doi = {10.18112/openneuro.ds002578.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds002578.v1.1.0},
}

About This Dataset#

Data for this selective attention task was collected in 2004 at the Swartz Center for Computational Neuroscience at UCSD. These datasets are part of a larger corpus of 32-channel data collected a few years prior. The experiment is identical although the number of channel is larger (256), the electrode positions are scanned and the anatomical MRI is provided (allowing for precise source localization). See publication for more details.

Raw data manipulation before export: - Fuse all BDF BIOSEMI files and reference to electrode 135 (see loadallbdf_2020.m) - Fuse with presentation file information (see loadallbdf_2020.m) - Remove spurious events of type ‘condition’ and ‘201’ (see clean_events.m) - Add HED tags (see addHEDTags.m) - Convert MRI to NIFTI format (MRIcron) and reorient (MRIcrogl) (see convert_nifti.m)

Dataset Information#

Dataset ID

DS002578

Title

Visual Oddball Task (256 channels)

Year

2020

Authors

Arnaud Delorme, Scott Makeig

License

CC0

Citation / DOI

10.18112/openneuro.ds002578.v1.1.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds002578,
  title = {Visual Oddball Task (256 channels)},
  author = {Arnaud Delorme and Scott Makeig},
  doi = {10.18112/openneuro.ds002578.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds002578.v1.1.0},
}

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: 2

  • Recordings: 22

  • Tasks: 1

Channels & sampling rate

  • Channels: 256

  • Sampling rate (Hz): 256.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 1.3 GB

  • File count: 22

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: 10.18112/openneuro.ds002578.v1.1.0

Provenance

API Reference#

Use the DS002578 class to access this dataset programmatically.

class eegdash.dataset.DS002578(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds002578. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 2; recordings: 2; 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/ds002578 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds002578

Examples

>>> from eegdash.dataset import DS002578
>>> dataset = DS002578(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#