DS005021: eeg dataset, 36 subjects#

Name: Tilt Illusion by Phase
Published: 2024
License: CC0

Tilt Illusion by Phase

Access recordings and metadata through EEGDash.

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

Modality: eeg Subjects: 36 Recordings: 36 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

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},
}

About This Dataset#

Overview

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.

Citing this dataset

View full README

Overview

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.

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.

Dataset Information#

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

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: 36
Recordings: 36
Tasks: 1

Channels & sampling rate

Channels: 72
Sampling rate (Hz): 1024.0
Duration (hours): 43.06305555555556

API Reference#

Use the DS005021 class to access this dataset programmatically.

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

Bases: EEGDashDataset

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, 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