DS004587#

IllusionGameEEG_data

Access recordings and metadata through EEGDash.

Citation: Makowski, Dominique, Te, An-Shu, Jiayi, Zhang, Kirk, Stephanie, Ngoi, Zi Liang (2023). IllusionGameEEG_data. 10.18112/openneuro.ds004587.v1.0.0

Modality: eeg Subjects: 103 Recordings: 1605 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004587

dataset = DS004587(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 = DS004587(cache_dir="./data", subject="01")

Advanced query

dataset = DS004587(
    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{ds004587,
  title = {IllusionGameEEG_data},
  author = {Makowski, Dominique and Te, An-Shu and Jiayi, Zhang and Kirk, Stephanie and Ngoi, Zi Liang},
  doi = {10.18112/openneuro.ds004587.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004587.v1.0.0},
}

About This Dataset#

Overview

This dataset was collected in 2022-20233 and comprises electroencephalography, physiological and behavioural data acquired from 103 healthy individuals (ages: 21-45). The task was administered as part of a larger study.

Task Description

View full README

Overview

This dataset was collected in 2022-20233 and comprises electroencephalography, physiological and behavioural data acquired from 103 healthy individuals (ages: 21-45). The task was administered as part of a larger study.

Task Description

Illusion Game (IG)

The aim of this task is to investigate people’s sensitivity to visual illusions as a general, common factor. Using Pyllusion, which enabled us to manipulate the objective parameters of visual illusions, we generated stimuli of varying task difficulty and illusion strength for 3 different classic illusions (Ebbinghaus, Müller-Lyer and Vertical-Horizontal). We then created an experimental task in which participants were instructed to make perceptual judgements about targets in the illusion as quickly as possible, ignoring its context, which biases their perception of the illusion. For instance, in the Müller-Lyer illusion, the same-length line segments (targets) appear to have different lengths if they end with inwards vs. outwards pointing arrows (context). The first series of the 3 illusion blocks (each comprising 64 trials) were first presented to participants in a randomized order, followed by a short break, after which participants performed the second series of blocks displayed in a newly randomized order. In total, each participant performed 384 illusion trials (6*64).

Resting State

Before the start of the illusion task, paricipants were instructed to keep their eyes closed for 8 minutes. At the end of the resting period, a ‘beep’ soundclip was played to cue participants to open their eyes. An adapted version of the Amsterdam Resting State Questionnaire (Diaz et al., 2014) was then administered to examine participants’ subjective resting state experience.

NOTES

Due to a technical error, sub-FFE111 and sub-FFE116 do not have any physiological data, and sub-FFE117, sub-FFE139 and sub-FFE146 do not have behavioural data for the illusion game task.

EEG data collection was split into 6 runs corresponding to each block of illusion trials for sub-FFE111 and sub-FFE121 during pilot testing.

EEG data collection was collected twice for sub-FFE007 due to a technical glitch that occcured in the middle of illusion task trials.

Data acquisition

EEG data acquisition

EEG signals were recorded using the EasyCap 64-channel and BrainVision Recording system. Electrodes were placed on the EEG cap according to the standard 10-5 system of electrode placement (Oostenveld & Praamsrta, 2001) and impedance was kept below 12 kOhm for each subject. The ground electrode was placed on the forehead the Cz was used as the reference channel. During recording, the sampling rate was 10000Hz. Note that channels Tp9 and Tp10 were placed near the outer canthi of each eye, and POz as well as Oz were fixed above and below one of the eyes to measure the E0G.

Physiological data acquisition

Participants’ physiological signals, that is their electrocardiogram (ECG), photoplethysmograph (PPG) and respiration signals (RSP), were obtained at a sampling frequency of 1000Hz. All physiological signals were recorded via the PLUX OpenSignals software and BITalino Toolkit.

ECG was collected using three ECG electrodes placed according to a modified Lead II configuration, and RSP was acquired using a respiration belt tightened over participants’ upper abdomen. PPG sensors, which record changes in blood volume, were clipped on the tip of the index finger of participants’ non-dominant hand to meaure heart rate and oxygen saturation.

References

Diaz, B. A., Van Der Sluis, S., Benjamins, J. S., Stoffers, D., Hardstone, R., Mansvelder, H. D., … & Linkenkaer-Hansen, K. (2014). The ARSQ 2.0 reveals age and personality effects on mind-wandering experiences. Frontiers in psychology, 5, 271.

Dataset Information#

Dataset ID	`DS004587`
Title	IllusionGameEEG_data
Year	2023
Authors	Makowski, Dominique, Te, An-Shu, Jiayi, Zhang, Kirk, Stephanie, Ngoi, Zi Liang
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004587.v1.0.0
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: 103
Recordings: 1605
Tasks: 1

Channels & sampling rate

Channels: 64 (114), 59 (114)
Sampling rate (Hz): 10000.0
Duration (hours): 0.0

API Reference#

Use the DS004587 class to access this dataset programmatically.

class eegdash.dataset.DS004587(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004587. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 103; recordings: 114; 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/ds004587 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004587

Examples

>>> from eegdash.dataset import DS004587
>>> dataset = DS004587(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