DS007537: eeg dataset, 23 subjects#

A multimodal dataset of EEG, eye-tracking, and physiological signals during smartphone interaction

Access recordings and metadata through EEGDash.

Citation: Prakash Mishra, Tapan K. Gandhi, Saurabh R. Gandhi (2026). A multimodal dataset of EEG, eye-tracking, and physiological signals during smartphone interaction. 10.18112/openneuro.ds007537.v1.0.0

Modality: eeg Subjects: 23 Recordings: 23 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007537

dataset = DS007537(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS007537(cache_dir="./data", subject="01")

Advanced query

dataset = DS007537(
    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{ds007537,
  title = {A multimodal dataset of EEG, eye-tracking, and physiological signals during smartphone interaction},
  author = {Prakash Mishra and Tapan K. Gandhi and Saurabh R. Gandhi},
  doi = {10.18112/openneuro.ds007537.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007537.v1.0.0},
}

About This Dataset#

This dataset contains multimodal physiological recordings acquired during smartphone interaction and video viewing conditions. The dataset includes simultaneous electroencephalography (EEG), eye-tracking, photoplethysmography (PPG), and galvanic skin response (GSR) signals.

Experimental Protocol

Participants complete two experimental conditions while wearing a 64-channel EEG cap and a head-mounted eye tracker, with simultaneous PPG and GSR recordings: Smartphone Monitor condition (10 min): Participants engage in naturalistic smartphone use using their personal devices. They are instructed to interact freely with one of their most frequently used applications (e.g., browsing, reading, or app interaction). No constraints are imposed on interaction style. Video Monitor condition (5 min): Participants view a standardized video presented on a monitor.

Participants

Number of participants: 23 (sub-01 to sub-23) Participants are healthy adults. Detailed demographic and experimental information (age, sex, and smartphone application type) is provided on participants.tsv at the root of the dataset.

Hardware Synchronization and Event Markers

Session boundaries are marked using hardware-based transistor–transistor logic (TTL) synchronization pulses. Five TTL pulses are delivered at ~1-second intervals to mark session onset and offset. Additional TTL pulses are generated every 20 seconds during the session. These triggers are embedded in both EEG and eye-tracking data streams, enabling precise temporal alignment across all modalities. EEG event markers are defined for two conditions: Smartphone Monitor (onset: S 12; offset: S 13) and Video Monitor (onset: S 22; offset: S 23). In the eye-tracking data, session boundaries are identified using TTL bursts consisting of five pulses occuring at ~1-second intervals with pulse characteristics (Type: sig; Direction: in; Value: 1.0). Additional synchronization pulses are present at ~20-second intervals during sessions.

Dataset Information#

Dataset ID

DS007537

Title

A multimodal dataset of EEG, eye-tracking, and physiological signals during smartphone interaction

Author (year)

—

Canonical

—

Importable as

DS007537

Year

2026

Authors

Prakash Mishra, Tapan K. Gandhi, Saurabh R. Gandhi

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds007537.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds007537,
  title = {A multimodal dataset of EEG, eye-tracking, and physiological signals during smartphone interaction},
  author = {Prakash Mishra and Tapan K. Gandhi and Saurabh R. Gandhi},
  doi = {10.18112/openneuro.ds007537.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007537.v1.0.0},
}

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: 23

  • Recordings: 23

  • Tasks: 1

Channels & sampling rate

  • Channels: 66

  • Sampling rate (Hz): 1000.0

  • Duration (hours): 6.638899166666667

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 6.1 GB

  • File count: 23

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds007537.v1.0.0

Provenance

API Reference#

Use the DS007537 class to access this dataset programmatically.

class eegdash.dataset.DS007537(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

A multimodal dataset of EEG, eye-tracking, and physiological signals during smartphone interaction

Study:

ds007537 (OpenNeuro)

Author (year):

nan

Canonical:

—

Also importable as: DS007537, nan.

Modality: eeg. Subjects: 23; recordings: 23; 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/ds007537 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007537 DOI: https://doi.org/10.18112/openneuro.ds007537.v1.0.0

Examples

>>> from eegdash.dataset import DS007537
>>> dataset = DS007537(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#