DS003061#

EEG data from an auditory oddball task

Access recordings and metadata through EEGDash.

Citation: Arnaud Delorme (2020). EEG data from an auditory oddball task. 10.18112/openneuro.ds003061.v1.1.2

Modality: eeg Subjects: 13 Recordings: 282 License: CC0 Source: openneuro Citations: 4.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003061

dataset = DS003061(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS003061(cache_dir="./data", subject="01")

Advanced query

dataset = DS003061(
    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{ds003061,
  title = {EEG data from an auditory oddball task},
  author = {Arnaud Delorme},
  doi = {10.18112/openneuro.ds003061.v1.1.2},
  url = {https://doi.org/10.18112/openneuro.ds003061.v1.1.2},
}

About This Dataset#

Data collection took place at the Meditation Research Institute (MRI) in Rishikesh, India under the supervision of Arnaud Delorme, PhD. The project was approved by the local MRI Indian ethical committee and the ethical committee of the University of California San Diego (IRB project # 090731).

Participants sat either on a blanket on the floor or on a chair for both experimental periods depending on their personal preference. They were asked to keep their eyes closed and all lighting in the room was turned off during data collection. An intercom allowed communication between the experimental and the recording room.

Participants performed three identical sessions of 13 minutes each. 750 stimuli were presented with 70% of them being standard (500 Hz pure tone lasting 60 milliseconds), 15% being oddball (1000 Hz pure tone lasting 60 ms) and 15% being distractors (1000 Hz white noise lasting 60 ms). All sounds took 5 milliseconds to ramp up and 5 milliseconds to ramp down. Sounds were presented at a rate of 1 per second with a random gaussian jitter of standard deviation 25 ms. Participants were instructed to respond to oddball by pressing a key on a keypad that was resting on their lap.

Data collection was performed with an Active Two Biosemi system (Biosemi, Inc.) at 1024Hz and 10-20 standard caps from the same company tailored to the subject’s head size. Stimuli were presented with the psychophysics MATLAB toolbox. The code for presenting stimuli and all the data is made available (see code and stimuli folder). Before making the data public, the raw data were resampled at 256 Hz using the standalone tool provided by Biosemi, then converted to the EEGLAB data format. No further data manipulation was performed.

Dataset Information#

Dataset ID

DS003061

Title

EEG data from an auditory oddball task

Year

2020

Authors

Arnaud Delorme

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds003061.v1.1.2

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds003061,
  title = {EEG data from an auditory oddball task},
  author = {Arnaud Delorme},
  doi = {10.18112/openneuro.ds003061.v1.1.2},
  url = {https://doi.org/10.18112/openneuro.ds003061.v1.1.2},
}

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: 13

  • Recordings: 282

  • Tasks: 1

Channels & sampling rate

  • Channels: 64 (39), 79 (39)

  • Sampling rate (Hz): 256.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 2.3 GB

  • File count: 282

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds003061.v1.1.2

Provenance

API Reference#

Use the DS003061 class to access this dataset programmatically.

class eegdash.dataset.DS003061(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003061. Modality: eeg; Experiment type: Perception. Subjects: 13; recordings: 39; 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/ds003061 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003061

Examples

>>> from eegdash.dataset import DS003061
>>> dataset = DS003061(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#