EEGdash›OpenNeuro›DS005021

Iss. 5021 · 36 subjects · 36 recordings · CC0

Dataset Brief · Tilt Illusion by Phase

DS005021: eeg dataset, 36 subjects#

Tilt Illusion by Phase

Citation: Jessica G. Williams, William J. Harrison, Henry A. Beale, Jason B. Mattingley, Anthony M. Harris (2024). Tilt Illusion by Phase. 10.18112/openneuro.ds005021.v1.2.1

36-participant EEG dataset — Tilt Illusion by Phase.

Data & curation Jessica G. Williams · William J. Harrison · Henry A. Beale · Jason B. Mattingley · Anthony M. Harris
Year 2024 · Distributed via OpenNeuro
Funding Australian Research Council (DE190100136) · Australian Research Council (DE220101019) · + 1 more

EEG · 72 ch1024 HzBIDS 1.2.1Task · tiltillusionHealthyVisualAttention

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 DS005021

dataset = DS005021(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 = DS005021(cache_dir="./data", subject="01")

Advanced query

dataset = DS005021(
    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{ds005021,
  title = {Tilt Illusion by Phase},
  author = {Jessica G. Williams and William J. Harrison and Henry A. Beale and Jason B. Mattingley and Anthony M. Harris},
  doi = {10.18112/openneuro.ds005021.v1.2.1},
  url = {https://doi.org/10.18112/openneuro.ds005021.v1.2.1},
}

§ 02Study · The README

About This Dataset#

This is the “Tilt Illusion” dataset.

In brief, it contains EEG data for 36 subjects responding to the percieved orientation

of a central target grating, that is titrated to appear vertical on average, and is surrounded by an anular grating of +-30 degrees. We then looked at the prestimulus EEG correlates of an increased or decreased tilt illusion.

Overview

Citing this dataset

Please cite as follows:

Williams, J.G., Harrison, W.J., Beale, H.A., Mattingley, J.B., & Harris, A.M. (2024). Effects of alpha oscillation power and phase on discrimination performance in a visual tilt illusion. Current Biology.

For more information, see the dataset_description.json file.

View full README

Overview

Citing this dataset

Please cite as follows:

Williams, J.G., Harrison, W.J., Beale, H.A., Mattingley, J.B., & Harris, A.M. (2024). Effects of alpha oscillation power and phase on discrimination performance in a visual tilt illusion. Current Biology.

For more information, see the dataset_description.json file.

License

The tilt illusion dataset is made available under the CC BY 4.0 license.

Copyright (c) 2024, Jessica Williams, William Harrison, Henry Beale, Jason Mattingley, & Anthony Harris A human readable information can be found at: https://creativecommons.org/licenses/by/4.0/deed.en

Format

The dataset is formatted according to the Brain Imaging Data Structure (BIDS).

See the dataset_description.json file for the specific version used. Generally, you can find metadata in the .tsv files and documentation thereof in the accompanying .json files.

An important BIDS definition to consider is the “Inheritance Principle”, which is described in the BIDS specification under the following link: https://bids-specification.readthedocs.io/en/latest/common-principles.html#the-inheritance-principle In brief, the Inheritance Pinciple states that any metadata file (such as .json, .tsv) may be defined at any directory level, but no more than one applicable file may be defined at a given level […], and the values from the top level are inherited by all lower levels – unless they are overridden by a file at the lower level.

Details about the experiment

For a detailed description of the task, see Williams et al. (2024) What follows is a brief summary.

Participants were seated in front of a computer screen placed on a desk. On each trial they were presented with a central target grating, surrounded by an annular grating of +-30 degrees. This induced a ‘tilt illusion’ whereby the percieved angle of the central grating was biased away from the angle of the surround. We first titrated the angle of the central grating to each participant’s percieved vertical angle, separately for each surround. Percieved vertical was defined as the angle at which the participant reported the grating as tilted leftward and rightward equally often. Participants responded with their right hand by pressing the left and right arrow keys on a standard USB keyboard. Stimuli were presented very briefly (8.3ms) at 60% contrast, and were clearly visible.

Between trials, a mask made from the combination of several gratings was presented to prevent the buildup of tilt aftereffects across trials.

Throughout the experiment, EEG data was recorded using a Biosemi Active 2 system with 64 scalp electrods and 6 EOG electrodes (left and right HEOG, VEOG on left eye, and left and right mastoids - in positions EXG 3-8).

For more information, you can also consult the events.tsv and events.json files. The original data was recorded in .bdf format using Actiview. It is stored in the /sourcedata directory. To comply with the BIDS format, the .bdf format was converted to EEGLab format, constituting a ‘.set’ file and a ‘fdt’ file for each dataset.

Participant 1’s data was corrupted by large artefacts that could not be corrected. Participants 8, 16, and 28 had no EEG data recorded, as their pre-task titration failed to converge. As such, the data for these 4 participants are not included in this dataset.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 72 ch (n=36 recordings)

Sampling frequencies: 1024.0 Hz (n=36 recordings)

Total recording duration: 47 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 72 ch · EEG · 1024 Hz · 36 subjects, 36 recordings

Live trace viewer — sub-13 · task-tiltillusion

Showing one representative recording out of 36 subjects and 36 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.DS005021(cache_dir, query=None, s3_bucket=None, **kwargs)

Bases: EEGDashDataset

Author (year)Williams2024

Canonical—

Importable asDS005021 · Williams2024

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

class eegdash.dataset.DS005021(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Tilt Illusion by Phase

Study:: ds005021 (OpenNeuro)
Author (year):: Williams2024
Canonical:: —

Also importable as: DS005021, Williams2024.

Modality: eeg. Subjects: 36; recordings: 36; 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/ds005021 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005021 DOI: https://doi.org/10.18112/openneuro.ds005021.v1.2.1 NEMAR citation count: 0

Examples

>>> from eegdash.dataset import DS005021
>>> dataset = DS005021(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/ds005021 · pull with datasets.load_dataset("EEGDash/ds005021").huggingface

Croissant 1.0Machine-readable JSON-LD descriptor — DS005021.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 ds005021 to reproduce the tutorial on this dataset.

Citation

Jessica G. Williams, William J. Harrison, Henry A. Beale, Jason B. Mattingley, Anthony M. Harris (2024). Tilt Illusion by Phase. 10.18112/openneuro.ds005021.v1.2.1

Provenance

¹Contributed to openneuro in BIDS format.

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

³Persistent identifier: 10.18112/openneuro.ds005021.v1.2.1.

Related & sibling datasets

DS004603EEG · 37 subj DS005089EEG · 36 subj DS006817EEG · 34 subj DS005273EEG · 33 subj DS005189EEG · 30 subj

+ 1 more — see See Also below →

BIDS

BIDS 1.2.1

Sidecars

events · eeg.json

Provenance

CC0 · 10.18112/openneuro.ds005021.v1.2.1

Machine-readable

schema.org/Dataset · Croissant

Mirrors

OpenNeuro · NEMAR · HuggingFace

Dataset ID	`DS005021`
Title	Tilt Illusion by Phase
Author (year)	`Williams2024`
Canonical	—
Importable as	`DS005021`, `Williams2024`
Year	2024
Authors	Jessica G. Williams, William J. Harrison, Henry A. Beale, Jason B. Mattingley, Anthony M. Harris
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005021.v1.2.1
Source links	OpenNeuro \| NeMAR \| Source URL

DS005021: eeg dataset, 36 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#