EEGdash›OpenNeuro›DS005662

Iss. 5662 · 80 subjects · 80 recordings · CC0

Dataset Brief · A comprehensive EEG dataset for investigating visual touch pe…

DS005662: eeg dataset, 80 subjects#

A comprehensive EEG dataset for investigating visual touch perception

Citation: Sophie Smit, Almudena Ramírez-Haro, Manuel Varlet, Denise Moerel, Genevieve L. Quek, Tijl Grootswagers (20). A comprehensive EEG dataset for investigating visual touch perception. 10.18112/openneuro.ds005662.v2.0.1

80-participant EEG dataset — A comprehensive EEG dataset for investigating visual touch perception.

Data & curation Sophie Smit · Almudena Ramírez-Haro · Manuel Varlet · Denise Moerel · Genevieve L. Quek · Tijl Grootswagers
Year 20 · Distributed via OpenNeuro
Funding This research was supported by Australian Research Council grants DP220103047 (MV) and DE230100380 (TG).

EEG · 65 ch2048 HzBIDS 1.0.0Task · videoHealthyVisualPerception

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 DS005662

dataset = DS005662(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 = DS005662(cache_dir="./data", subject="01")

Advanced query

dataset = DS005662(
    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{ds005662,
  title = {A comprehensive EEG dataset for investigating visual touch perception},
  author = {Sophie Smit and Almudena Ramírez-Haro and Manuel Varlet and Denise Moerel and Genevieve L. Quek and Tijl Grootswagers},
  doi = {10.18112/openneuro.ds005662.v2.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005662.v2.0.1},
}

§ 02Study · The README

About This Dataset#

Data collection took place at The MARCS Institute for Brain, Behaviour and Development in Sydney, Australia. The study was approved by the Western Sydney University Ethics Committee.

We recorded EEG data while participants viewed rapid streams of videos adapted from the Validated Touch-Video Database (Smit & Rich, 2025) depicting touch to a hand. Both the adapted videos used in this project, and original videos and validation data, are available on OSF (https://osf.io/jvkqa/).

There were 32 sequences in total with a total of 2880 non-target trials (90 unique videos, each presented 8 times) alongside a variable number of target trials (showing touch to an object). Between trials there was an inter-trial-interval of 200ms. The experimental task lasted approximately 55 minutes including breaks. We also recorded questionnaire responses.

Whole brain 64-channel EEG data were recorded using an Active Two Biosemi system (Biosemi, Inc.) at 2048Hz and 10-20 standard caps. Stimuli were presented using Python and PsychoPy software version 2023.3.1.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Age distribution by gender (n=80, range 18–76 yr, mean 30.1 yr)

152025303540455075

Female · 54Male · 24Other · 2

Sex composition

Female
54
Male
24
Other
2
F : M ratio
2.25 : 1

68% female · n = 80 subjects with reported sex.

HandednessAmbidextrous · 3

Channel counts: 65 ch (n=80 recordings)

Sampling frequencies: 2048.0 Hz (n=80 recordings)

Total recording duration: 80 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 65 ch · EEG · 2048 Hz · 80 subjects, 80 recordings

Live trace viewer — sub-13 · task-video

Showing one representative recording out of 80 subjects and 80 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.DS005662(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Smit2024

Canonical—

Importable asDS005662 · Smit2024

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

class eegdash.dataset.DS005662(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

A comprehensive EEG dataset for investigating visual touch perception

Study:: ds005662 (OpenNeuro)
Author (year):: Smit2024
Canonical:: —

Also importable as: DS005662, Smit2024.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 80; recordings: 80; 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/ds005662 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005662 DOI: https://doi.org/10.18112/openneuro.ds005662.v2.0.1

Examples

>>> from eegdash.dataset import DS005662
>>> dataset = DS005662(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/ds005662 · pull with datasets.load_dataset("EEGDash/ds005662").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS005662.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 ds005662 to reproduce the tutorial on this dataset.

Citation

Sophie Smit, Almudena Ramírez-Haro, Manuel Varlet, Denise Moerel, Genevieve L. Quek, … (20). A comprehensive EEG dataset for investigating visual touch perception. 10.18112/openneuro.ds005662.v2.0.1

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds005662.v2.0.1.

Related & sibling datasets

DS005648EEG · 21 subj DS005363EEG · 43 subj DS007454EEG · 42 subj DS007162EEG · 34 subj DS005946EEG · 39 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.0.0

Sidecars

events

Provenance

CC0 · 10.18112/openneuro.ds005662.v2.0.1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS005662`
Title	A comprehensive EEG dataset for investigating visual touch perception
Author (year)	`Smit2024`
Canonical	—
Importable as	`DS005662`, `Smit2024`
Year	20
Authors	Sophie Smit, Almudena Ramírez-Haro, Manuel Varlet, Denise Moerel, Genevieve L. Quek, Tijl Grootswagers
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005662.v2.0.1
Source links	OpenNeuro \| NeMAR \| Source URL

DS005662: eeg dataset, 80 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#