DS004582#

FakeFaceEmo_data

Access recordings and metadata through EEGDash.

Citation: Makowski, Dominique, Te, An-Shu, Kirk, Stephanie, Ngoi, Zi Liang (2023). FakeFaceEmo_data. 10.18112/openneuro.ds004582.v1.0.0

Modality: eeg Subjects: 73 Recordings: 881 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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#

Overview

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.

Task Description

View full README

Overview

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.

Task Description

Fake Face (FF)

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.

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

Dataset Information#

Dataset ID	`DS004582`
Title	FakeFaceEmo_data
Year	2023
Authors	Makowski, Dominique, Te, An-Shu, Kirk, Stephanie, Ngoi, Zi Liang
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004582.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: 73
Recordings: 881
Tasks: 1

Channels & sampling rate

Channels: 64 (73), 59 (73)
Sampling rate (Hz): 10000.0
Duration (hours): 0.0

API Reference#

Use the DS004582 class to access this dataset programmatically.

class eegdash.dataset.DS004582(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004582. Modality: eeg; Experiment type: Affect; Subject type: Healthy. 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

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/ds004582 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004582

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, 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