EEGdash›NeMAR›ON008302

Iss. 8302 · 124 subjects · 370 recordings · CC0

Dataset Brief · Cognitive overload and rest dataset. Simultaneous EEG + PPG +…

ON008302: eeg dataset, 124 subjects#

Cognitive overload and rest dataset. Simultaneous EEG + PPG + ECG + eye-tracking

Access recordings and metadata through EEGDash.

Citation: Yuri G. Pavlov (20). Cognitive overload and rest dataset. Simultaneous EEG + PPG + ECG + eye-tracking. 10.82901/nemar.on008302

Modality: eeg Subjects: 124 Recordings: 370 License: CC0 Source: nemar

Metadata: Complete (100%)

124-participant EEG dataset — Cognitive overload and rest dataset. Simultaneous EEG + PPG + ECG + eye-tracking.

Data & curation Yuri G. Pavlov
Senior author Yuri G. Pavlov
Year 20 · Distributed via NeMAR

EEG · 65 ch1000 HzBIDS 1.10.03 tasks

Layer 01Study

What was asked

Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.

Layer 02Signal · BIDS

What was recorded

Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.

Layer 03Training · ML

What you can train on

Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.

§ 01Access · Get started

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import ON008302

dataset = ON008302(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 = ON008302(cache_dir="./data", subject="01")

Advanced query

dataset = ON008302(
    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{on008302,
  title = {Cognitive overload and rest dataset. Simultaneous EEG + PPG + ECG + eye-tracking},
  author = {Yuri G. Pavlov},
  doi = {10.82901/nemar.on008302},
  url = {https://doi.org/10.82901/nemar.on008302},
}

§ 02Study · The README

About This Dataset#

This dataset consists of raw 64-channel EEG, electrocardiography (ECG), photoplethysmography (PPG), eye-tracking/pupillometry, and behavioral data recorded from 124 healthy young adults during two experimental conditions: resting state and a verbal working memory digit-span task with serial recall.

During the resting-state session, participants alternated between four 1-minute blocks of eyes-closed and eyes-open rest. This was followed by 3 minutes and 52 seconds of passive cartoon watching (“The Man Who Was Afraid of Falling”, 2011).

DOI

Overview

Verbal working memory task

In the verbal working memory task - adaptive digit span, participants were presented visually with sequences of digits and recalled them in serial order. Sequence length was adaptive and depended on each participant’s performance. Trials were grouped into blocks of 10 trials. If a participant correctly recalled 7 or more trials in a block, the sequence length increased in the next block. If fewer than 7 trials were recalled correctly, the sequence length decreased in the next block.

Apparatus

Several modalities were recorded simultaneously during the resting-state and working memory tasks. EEG was recorded using a 64-channel EEG system (Brain Products). ECG, PPG were recorded from AUX channels of the Brain Products amplifier. Eye-tracking/pupillometry was recorded with Pupil Labs Core wearable eye-tracker. Behavioral responses were collected during the digit-span task - participants typed their responses using numpad. Correctness of each response - each individual digit recall in the sequence - is stored in the event triggers.

Task organization

Participants completed a resting-state recording followed by the verbal working memory task. The resting-state recording included alternating eyes-closed and eyes-open blocks, followed by passive video watching. The working memory task consisted of adaptive digit-span trials with serial recall.

Missing data

Missing memory task EEG IDs: sub-008, sub-035 Missing PPG IDs: sub-008, sub-035, sub-012, sub-015, sub-060 Missing eye-tracking/pupillometry IDs: sub-044, sub-096

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=124, range 18–32 yr, mean 20.5 yr)

15202530

Female · 100Male · 24

Sex composition

Female
100
Male
24
F : M ratio
4.17 : 1

81% female · n = 124 subjects with reported sex.

HandednessRight · 110Left · 10Ambidextrous · 4

Channel counts: 65 ch (n=370 recordings)

Sampling frequencies: 1000.0 Hz (n=370 recordings)

Total recording duration: 223 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 65 ch · EEG · 1000 Hz · 124 subjects, 370 recordings

Live trace viewer — sub-130 · task-memory

Showing one representative recording out of 124 subjects and 370 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 · 63 sensors — 63 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

§ 05Manifest · BIDS tree

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.

Recordings—

Files—

Subjects—

Modalities—

Click to load file structure…

§ 06API · Programmatic access

API Reference#

Signature

eegdash.dataset

class

eegdash.dataset.ON008302(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)—

Canonical—

Importable asON008302

Sourceeegdash/dataset/registry.py · [source ↗]

class eegdash.dataset.ON008302(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Cognitive overload and rest dataset. Simultaneous EEG + PPG + ECG + eye-tracking

Study:: on008302 (NeMAR)
Author (year):: —
Canonical:: —

Also importable as: ON008302.

Modality: eeg; Subject type: Unknown. Subjects: 124; recordings: 370; tasks: 3.

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/on008302 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on008302 DOI: https://doi.org/10.82901/nemar.on008302

Examples

>>> from eegdash.dataset import ON008302
>>> dataset = ON008302(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. If a dataset still reads its data lazily from one of them, a ValueError is raised before anything is deleted: load the data into memory first (preload=True) or save to another directory.
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.

Access modesMNE → braindecode → PyTorch → ML

.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne

BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode

DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch

Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr

Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — ON008302.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons

Examples using EEGDashcurated · start here

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 on008302 to reproduce the tutorial on this dataset.

Citation

Yuri G. Pavlov (20). Cognitive overload and rest dataset. Simultaneous EEG + PPG + ECG + eye-tracking. 10.82901/nemar.on008302

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.82901/nemar.on008302.

Related & sibling datasets

NM000304EEG · 54 subj ON007327EEG · 35 subj NM000335EEG · 38 subj ON008968EEG · 25 subj NM000629EEG · 40 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.10.0

Sidecars

events · channels · electrodes · coordsystem · eeg.json

Provenance

CC0 · 10.82901/nemar.on008302

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HF org

Dataset ID	`ON008302`
Title	Cognitive overload and rest dataset. Simultaneous EEG + PPG + ECG + eye-tracking
Author (year)	—
Canonical	—
Importable as	`ON008302`
Year	20
Authors	Yuri G. Pavlov
License	CC0
Citation / DOI	10.82901/nemar.on008302
Source links	OpenNeuro \| NeMAR \| Source URL

ON008302: eeg dataset, 124 subjects#

Quickstart#

About This Dataset#

Cohort#

Dataset Statistics#

Signal · Electrodes & live trace#

NEMAR Processing Statistics#

Manifest#

File Explorer#

API Reference#

Citation

Provenance

Related & sibling datasets

See Also#