DS004587: eeg dataset, 103 subjects#
IllusionGameEEG_data
Citation: Makowski, Dominique, Te, An-Shu, Jiayi, Zhang, Kirk, Stephanie, Ngoi, Zi Liang (2014). IllusionGameEEG_data. 10.18112/openneuro.ds004587.v1.0.0
103-participant EEG dataset — IllusionGameEEG_data.
Quickstart#
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)
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'])
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#
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.
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).
Overview
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.
Cohort#
Dataset Statistics#
Age distribution by gender (n=103, range 21–40 yr, mean 26.2 yr)
Sex composition
Channel counts: 64 ch (n=114 recordings)
Sampling frequencies: 10000.0 Hz (n=114 recordings)
Total recording duration: 25 h
Signal · Electrodes & live trace#
Live trace viewer — sub-FFE150 · ses-01 · task-IG · run-01
Showing one representative recording out of
103 subjects and 114 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.
Electrode layout — EEG · 59 sensors — 59 channels
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
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.
Full dataset metadata table
Dataset ID |
|
Title |
IllusionGameEEG_data |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2014 |
Authors |
Makowski, Dominique, Te, An-Shu, Jiayi, Zhang, Kirk, Stephanie, Ngoi, Zi Liang |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004587 · Makowski2023_IllusionGameEEGeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004587(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
IllusionGameEEG_data
- Study:
ds004587(OpenNeuro)- Author (year):
Makowski2023_IllusionGameEEG- Canonical:
—
Also importable as:
DS004587,Makowski2023_IllusionGameEEG.Modality:
eeg. 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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand 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 DOI: https://doi.org/10.18112/openneuro.ds004587.v1.0.0 NEMAR citation count: 0
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: 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.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds004587").huggingface
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 ds004587 to reproduce the tutorial on this dataset.
Citation
Makowski, Dominique, Te, An-Shu, Jiayi, Zhang, Kirk, Stephanie, Ngoi, Zi Liang (2014). IllusionGameEEG_data. 10.18112/openneuro.ds004587.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004587.v1.0.0.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset