DS003838#

EEG, pupillometry, ECG and photoplethysmography, and behavioral data in the digit span task and rest

Access recordings and metadata through EEGDash.

Citation: Yuri G. Pavlov, Dauren Kasanov, Alexandra I. Kosachenko, Alexander I. Kotyusov (2021). EEG, pupillometry, ECG and photoplethysmography, and behavioral data in the digit span task and rest. 10.18112/openneuro.ds003838.v1.0.6

Modality: eeg Subjects: 65 Recordings: 947 License: CC0 Source: openneuro Citations: 7.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS003838

dataset = DS003838(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 = DS003838(cache_dir="./data", subject="01")

Advanced query

dataset = DS003838(
    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{ds003838,
  title = {EEG, pupillometry, ECG and photoplethysmography, and behavioral data in the digit span task and rest},
  author = {Yuri G. Pavlov and Dauren Kasanov and Alexandra I. Kosachenko and Alexander I. Kotyusov},
  doi = {10.18112/openneuro.ds003838.v1.0.6},
  url = {https://doi.org/10.18112/openneuro.ds003838.v1.0.6},
}

About This Dataset#

This dataset consists of raw 64-channel EEG, cardiovascular (electrocardiography and photoplethysmography), and pupillometry data from 86 human participants during 4 minutes of eyes-closed resting and during performance of a classic working memory task – digit span task with serial recall. The participants either memorized (memory) or just listened to (control condition) sequences of 5, 9, or 13 digits presented auditorily with 2 second stimulus onset asynchrony. The dataset can be used for (1) developing algorithms for cognitive load discrimination and detection of cognitive overload; (2) studying neural (event-related potentials and brain oscillations) and peripheral physiological (electrocardiography, photoplethysmography, and pupillometry) signals during encoding and maintenance of each sequentially presented memory item in a fine time scale; (3) correlating cognitive load and individual differences in working memory to neural and peripheral physiology, and studying the relationship between the physiological signals; (4) integration of the physiological findings with the vast knowledge coming from behavioral studies of verbal working memory in simple span paradigms.

EEG, pupillometry, ECG and photoplethysmography, and behavioral data are stored separately in corresponding folders. Each data record can consist of four data folders: beh - behavioral data: correctness of the recall in the memory trials ecg - electrocardiography (ECG) and photoplethysmography (PPG) data eeg - EEG data pupil - pupillometry and eye-tracking data

Some of the participants had some physiological data missing: sub-017, sub-094 have no pupillometry data sub-017, sub-037, sub-066 have no ECG and PPG data sub-013, sub-014, sub-015, sub-016, sub-017, sub-018, sub-019, sub-020, sub-021, sub-022, sub-023, sub-024, sub-025, sub-026, sub-027, sub-028, sub-029, sub-030, sub-031, sub-037, sub-066 have no EEG data

Dataset Information#

Dataset ID	`DS003838`
Title	EEG, pupillometry, ECG and photoplethysmography, and behavioral data in the digit span task and rest
Year	2021
Authors	Yuri G. Pavlov, Dauren Kasanov, Alexandra I. Kosachenko, Alexander I. Kotyusov
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds003838.v1.0.6
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: 65
Recordings: 947
Tasks: 1

Channels & sampling rate

Channels: 63
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS003838 class to access this dataset programmatically.

class eegdash.dataset.DS003838(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds003838. Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 65; recordings: 130; tasks: 2.

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/ds003838 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds003838

Examples

>>> from eegdash.dataset import DS003838
>>> dataset = DS003838(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