DS004511#

Deception_data

Access recordings and metadata through EEGDash.

Citation: Makowski, Dominique, Pham, Tam, Lau, Zen Juen (2023). Deception_data. 10.18112/openneuro.ds004511.v1.0.2

Modality: eeg Subjects: 45 Recordings: 993 License: CC0 Source: openneuro Citations: 2.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004511

dataset = DS004511(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 = DS004511(cache_dir="./data", subject="01")

Advanced query

dataset = DS004511(
    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{ds004511,
  title = {Deception_data},
  author = {Makowski, Dominique and Pham, Tam and Lau, Zen Juen},
  doi = {10.18112/openneuro.ds004511.v1.0.2},
  url = {https://doi.org/10.18112/openneuro.ds004511.v1.0.2},
}

About This Dataset#

Overview

This dataset was collected in 2020 and comprises electroencephalography, physiological and behavioural data. The dataset includes both resting-state (eyes closed) and task-related neurophysiological signals acquired from 44 healthy individuals (ages: 21-40). The tasks administered to subjects include a spontaneous deception task (Gambling Game; GG) as well as a task assessing cognitive control (CC).

Task Description

View full README

Overview

This dataset was collected in 2020 and comprises electroencephalography, physiological and behavioural data. The dataset includes both resting-state (eyes closed) and task-related neurophysiological signals acquired from 44 healthy individuals (ages: 21-40). The tasks administered to subjects include a spontaneous deception task (Gambling Game; GG) as well as a task assessing cognitive control (CC).

Task Description

Spontaneous Deception Task (GG)

Participants were informed that the GG task aimed to study a player’s behaviour during a gambling game. They were given SGD 50 at the start of the game. They were to undergo 144 rounds of making a prediction about the outcome of a dice roll. They were to also place a bet ranging from 10 cents to 80 cents for each prediction; they win the bet if the prediction was true and lose it if it was false.

Participants were also informed that they were the only ones who knew the outcome of the dice roll and were responsible for reporting if their predictions were true to the system, and were debriefed at the end regarding this cover story.

Cognitive Control (CC)

Participants performed 60 trials of a simple processing speed task, 80 trials of a simple response selection task, 160 trials of a response inhibition task, and 160 trials of a conflict resolution task. See details of the task neuropsychology/CognitiveControl.

Data acquisition

EEG data acquisition

EEG signals were recorded using the TruScan 128 Research EEG system and TruScan Aquisition software (DeyMed Diagnostics s.r.o). 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 20 kOhm for each subject. The ground electrode was placed on the zygomatic bone and two electrodes were fixed on the mastoids to be used as references. During recording, the sampling rate was 3000Hz. Note that channels 124 and 125 were placed above and below the eyes respectively for vertical EOG signals.

Note

sub-S200203 does not have any EEG acquisition file pertaining to the Gambling Game task due to technical errors during the recording.

Physiological data acquisition

Participants’ physiological signals, that is their electrocardiogram (ECG), respiration signals (RSP), electrodermal activity (EDA) and electromyography (EMG), were obtained at a sampling frequency of 4000Hz. All physiological signals were recorded via the BioPac MP160 system (BioPac Systems Inc., USA) and the AcqKnowledge 5.0 software.

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. EDA, a measure of skin conductance, was acquired using electrodes placed on the middle and index fingers of subjects’ non-dominant hands and EMG was obtained by measuring the electrical activity of the corrugator muscles.

Note

With regards to the Cognitive Control task, physiological data was collected over 2 sessions for sub-S200303 as a result of technical errors during the recording.

Dataset Information#

Dataset ID	`DS004511`
Title	Deception_data
Year	2023
Authors	Makowski, Dominique, Pham, Tam, Lau, Zen Juen
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004511.v1.0.2
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: 45
Recordings: 993
Tasks: 3

Channels & sampling rate

Channels: 139
Sampling rate (Hz): 3000.0
Duration (hours): 0.0

API Reference#

Use the DS004511 class to access this dataset programmatically.

class eegdash.dataset.DS004511(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004511. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 45; recordings: 134; tasks: 3.

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/ds004511 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004511

Examples

>>> from eegdash.dataset import DS004511
>>> dataset = DS004511(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