DS007537: eeg dataset, 23 subjects#
A multimodal dataset of EEG, eye-tracking, and physiological signals during smartphone interaction
Citation: Prakash Mishra, Tapan K. Gandhi, Saurabh R. Gandhi (—). A multimodal dataset of EEG, eye-tracking, and physiological signals during smartphone interaction. 10.18112/openneuro.ds007537.v1.0.0
23-participant EEG dataset — A multimodal dataset of EEG, eye-tracking, and physiological signals during smartphone interaction.
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.
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.
Cohort#
Dataset Statistics#
Age distribution by gender (n=23, range 20–32 yr, mean 24.3 yr)
Sex composition
Channel counts: 66 ch (n=23 recordings)
Sampling frequencies: 1000.0 Hz (n=23 recordings)
Total recording duration: 6 h 38 min
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-phoneuse
Showing one representative recording out of
23 subjects and 23 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
Electrode layout — EEG · 64 sensors — 64 channels
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
Manifest#
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.
Full dataset metadata table
Dataset ID |
|
Title |
A multimodal dataset of EEG, eye-tracking, and physiological signals during smartphone interaction |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Prakash Mishra, Tapan K. Gandhi, Saurabh R. Gandhi |
License |
CC0 |
Citation / DOI |
|
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},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.DS007537(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand 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)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorch
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for ds007537 to reproduce the tutorial on this dataset.
Citation
Prakash Mishra, Tapan K. Gandhi, Saurabh R. Gandhi (n.d.). A multimodal dataset of EEG, eye-tracking, and physiological signals during smartphone interaction. 10.18112/openneuro.ds007537.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds007537.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset