DS007322: eeg dataset, 57 subjects#

Personalized smartphone notifications bias auditory salience across processing stages

Access recordings and metadata through EEGDash.

Citation: Prakash Mishra, Tapan K Gandhi, Saurabh R. Gandhi (2026). Personalized smartphone notifications bias auditory salience across processing stages. 10.18112/openneuro.ds007322.v1.0.1

Modality: eeg Subjects: 57 Recordings: 57 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007322

dataset = DS007322(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS007322(cache_dir="./data", subject="01")

Advanced query

dataset = DS007322(
    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{ds007322,
  title = {Personalized smartphone notifications bias auditory salience across processing stages},
  author = {Prakash Mishra and Tapan K Gandhi and Saurabh R. Gandhi},
  doi = {10.18112/openneuro.ds007322.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds007322.v1.0.1},
}

About This Dataset#

Auditory Oddball Experiment to understand how Personalized smartphone notifications bias auditory salience across processing stages

Dataset Information#

Dataset ID

DS007322

Title

Personalized smartphone notifications bias auditory salience across processing stages

Author (year)

Mishra2026

Canonical

Mishra2024

Importable as

DS007322, Mishra2026, Mishra2024

Year

2026

Authors

Prakash Mishra, Tapan K Gandhi, Saurabh R. Gandhi

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds007322.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds007322,
  title = {Personalized smartphone notifications bias auditory salience across processing stages},
  author = {Prakash Mishra and Tapan K Gandhi and Saurabh R. Gandhi},
  doi = {10.18112/openneuro.ds007322.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds007322.v1.0.1},
}

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: 57

  • Recordings: 57

  • Tasks: 1

Channels & sampling rate

  • Channels: 64 (31), 66 (26)

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 48.70112305555556

Tags

  • Pathology: Healthy

  • Modality: Auditory

  • Type: Attention

Files & format

  • Size on disk: 42.5 GB

  • File count: 57

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds007322.v1.0.1

Provenance

API Reference#

Use the DS007322 class to access this dataset programmatically.

class eegdash.dataset.DS007322(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Personalized smartphone notifications bias auditory salience across processing stages

Study:

ds007322 (OpenNeuro)

Author (year):

Mishra2026

Canonical:

Mishra2024

Also importable as: DS007322, Mishra2026, Mishra2024.

Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 57; recordings: 57; 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/ds007322 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007322 DOI: https://doi.org/10.18112/openneuro.ds007322.v1.0.1

Examples

>>> from eegdash.dataset import DS007322
>>> dataset = DS007322(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#