DS004582: eeg dataset, 73 subjects#
FakeFaceEmo_data
Citation: Makowski, Dominique, Te, An-Shu, Kirk, Stephanie, Ngoi, Zi Liang (2019). FakeFaceEmo_data. 10.18112/openneuro.ds004582.v1.0.0
73-participant EEG dataset — FakeFaceEmo_data.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004582
dataset = DS004582(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004582(cache_dir="./data", subject="01")
Advanced query
dataset = DS004582(
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{ds004582,
title = {FakeFaceEmo_data},
author = {Makowski, Dominique and Te, An-Shu and Kirk, Stephanie and Ngoi, Zi Liang},
doi = {10.18112/openneuro.ds004582.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004582.v1.0.0},
}
About This Dataset#
This dataset was collected in 2023 and comprises electroencephalography, physiological and behavioural data acquired from 73 healthy individuals (ages: 21-45). The task was administered as part of a larger study.
The objective of the study was to investigate if emotional arousal would affect people’s perceived realness of others’ faces, given ambiguous information. To manipulate participants’ emotional arousal, images of angry (high emotionality) and neutral (low emotionality) faces (selected based on the their rated intensity from the NimStim Set of Facial Expressions (Tottenham et al., 2009)), were used as subliminal primes and facial images from the Multi-Racial Mega-Resolution database (Strohminger et al., 2016) were used as target stimuli. Blank screens were flashed prior to the target presentation in control trials. Forward and backward masks, generated by scrambling the primes, were implemented to prevent the primes from breaking awareness.
Overview
Each participant underwent a total of 222 trials, comprising of a forward mask,followed by the prime and backward mask, before the presentation of the target stimuli. The primes and targets were presented in a randomized order and trials were administered over a course of 3 blocks, between which participants were given a break to rest before proceeding to the next block of trials. During the presentation of the target stimulus, participants were instructed to indicate whether they thought the target was real or fake in a limited span of time (750ms), after which participants rated their confidence in their response using a sliding scale (0-100).
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
Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8
Cohort#
Dataset Statistics#
Age distribution by gender (n=73, range 21–40 yr, mean 26.3 yr)
Sex composition
Channel counts: 64 ch (n=73 recordings)
Sampling frequencies: 10000.0 Hz (n=73 recordings)
Total recording duration: 34 h
Signal · Electrodes & live trace#
Live trace viewer — sub-FFE150 · ses-01 · task-FF · run-01
Showing one representative recording out of
73 subjects and 73 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 |
FakeFaceEmo_data |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2019 |
Authors |
Makowski, Dominique, Te, An-Shu, Kirk, Stephanie, Ngoi, Zi Liang |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004582,
title = {FakeFaceEmo_data},
author = {Makowski, Dominique and Te, An-Shu and Kirk, Stephanie and Ngoi, Zi Liang},
doi = {10.18112/openneuro.ds004582.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds004582.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004582 · Makowski2023_FakeFaceEmoeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004582(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
FakeFaceEmo_data
- Study:
ds004582(OpenNeuro)- Author (year):
Makowski2023_FakeFaceEmo- Canonical:
—
Also importable as:
DS004582,Makowski2023_FakeFaceEmo.Modality:
eeg. Subjects: 73; recordings: 73; 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/ds004582 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004582 DOI: https://doi.org/10.18112/openneuro.ds004582.v1.0.0 NEMAR citation count: 0
Examples
>>> from eegdash.dataset import DS004582 >>> dataset = DS004582(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/ds004582").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 ds004582 to reproduce the tutorial on this dataset.
Citation
Makowski, Dominique, Te, An-Shu, Kirk, Stephanie, Ngoi, Zi Liang (2019). FakeFaceEmo_data. 10.18112/openneuro.ds004582.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.ds004582.v1.0.0.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset