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#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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

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

Electrode Layout#

Electrode layout — EEG · 64 sensors — 64 channels

Dataset Statistics#

Age distribution (n=23, range 20–32 yr)

202530

Sex distribution

8

15

Female Male Total: 23

Channel counts: 66 ch (n=23 recordings)

Sampling frequencies: 1000.0 Hz (n=23 recordings)

Total recording duration: 6 h 38 min

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 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: 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)