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#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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	—
Importable as	`DS007322`, `Mishra2026`
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

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

Electrode Layout#

Electrode layout — EEG · 64 sensors — 64 channels

Dataset Statistics#

Age distribution (n=57, range 18–48 yr)

152025303545

Sex distribution

14

43

Female Male Total: 57

Channel counts (ch)

6466

Sampling frequencies: 1000.0 Hz (n=57 recordings)

Total recording duration: 48 h

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

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 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:: —

Also importable as: DS007322, Mishra2026.

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: 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)