EEGdash›OpenNeuro›DS005416

Iss. 5416 · 23 subjects · 23 recordings · CC0

Dataset Brief · Fatigue Characterization of EEG under Mixed Reality Stereo Vi…

DS005416: eeg dataset, 23 subjects#

Fatigue Characterization of EEG under Mixed Reality Stereo Vision

Citation: Yan Wu, Chunguang Tao, Qi Li (—). Fatigue Characterization of EEG under Mixed Reality Stereo Vision. 10.18112/openneuro.ds005416.v1.0.1

23-participant EEG dataset — Fatigue Characterization of EEG under Mixed Reality Stereo Vision.

Data & curation Yan Wu · Chunguang Tao · Qi Li
Funding Jilin Scientific and Technology Development Program (grant no. 20240101358JC)

EEG · 64 ch1000 HzBIDS 1.1.1Task · WatchingTaskHealthyVisualResting-state

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 DS005416

dataset = DS005416(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 = DS005416(cache_dir="./data", subject="01")

Advanced query

dataset = DS005416(
    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{ds005416,
  title = {Fatigue Characterization of EEG under Mixed Reality Stereo Vision},
  author = {Yan Wu and Chunguang Tao and Qi Li},
  doi = {10.18112/openneuro.ds005416.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005416.v1.0.1},
}

§ 02Study · The README

About This Dataset#

In this study, we selected 24 electrodes for EEG recording: Fp1, Fp2, AF3, AF4, F7, Fz, F8, FC5, FC6 (frontal), FT7, FT8 (temporal), C3, Cz, C4, CP3, CP4 (central), P3, Pz, P4, PO3, PO4 (parietal), and O1, Oz, O2 (occipital).

Each participant was required to complete watching 2 resting scenes and 15 movement scenes.

A rating scene appeared to rate each exercise scene watched.

Each movement scene consisted of 20 trials of reciprocal periodic movements at a fixed depth and velocity. We focused on analyzing EEG data from watching resting scenes.

Researchers can use this EEG data to do resting-state analysis (corresponding to events ‘11’ and ‘13’) as well as task-state analysis (corresponding to event ‘12’).

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution (n=23, range 22–27 yr, mean 23.9 yr · sex per subject not reported)

2025

Sex composition

Female
10
Male
13
F : M ratio
0.77 : 1

43% female · n = 23 subjects with reported sex.

Channel counts: 64 ch (n=23 recordings)

Sampling frequencies: 1000.0 Hz (n=23 recordings)

Total recording duration: 24 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 64 ch · EEG · 1000 Hz · 23 subjects, 23 recordings

Live trace viewer — sub-s07 · task-WatchingTask

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.

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

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.DS005416(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Wu2024

Canonical—

Importable asDS005416 · Wu2024

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

class eegdash.dataset.DS005416(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Fatigue Characterization of EEG under Mixed Reality Stereo Vision

Study:: ds005416 (OpenNeuro)
Author (year):: Wu2024
Canonical:: —

Also importable as: DS005416, Wu2024.

Modality: eeg; Experiment type: Resting-state; Subject type: Healthy. 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/ds005416 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005416 DOI: https://doi.org/10.18112/openneuro.ds005416.v1.0.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005416
>>> dataset = DS005416(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.

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 FacePre-bundled mirror at EEGDash/ds005416 · pull with datasets.load_dataset("EEGDash/ds005416").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS005416.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 ds005416 to reproduce the tutorial on this dataset.

Citation

Yan Wu, Chunguang Tao, Qi Li (n.d.). Fatigue Characterization of EEG under Mixed Reality Stereo Vision. 10.18112/openneuro.ds005416.v1.0.1

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds005416.v1.0.1.

Related & sibling datasets

DS002181EEG · 226 subj DS005586EEG · 23 subj DS005642EEG · 21 subj DS005520EEG · 23 subj DS005406EEG · 29 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.1.1

Sidecars

events · events.json · channels · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds005416.v1.0.1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS005416`
Title	Fatigue Characterization of EEG under Mixed Reality Stereo Vision
Author (year)	`Wu2024`
Canonical	—
Importable as	`DS005416`, `Wu2024`
Year	—
Authors	Yan Wu, Chunguang Tao, Qi Li
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005416.v1.0.1
Source links	OpenNeuro \| NeMAR \| Source URL

DS005416: eeg dataset, 23 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#