DS004511: eeg dataset, 45 subjects#
Deception_data
Citation: Makowski, Dominique, Pham, Tam, Lau, Zen Juen (20). Deception_data. 10.18112/openneuro.ds004511.v1.0.2
45-participant EEG dataset — Deception_data.
Quickstart#
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)
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'])
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#
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).
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.
Overview
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.
Cohort#
Dataset Statistics#
Age distribution by gender (n=43, range 20–43 yr, mean 25.2 yr)
Sex composition
Channel counts: 139 ch (n=134 recordings)
Sampling frequencies: 3000.0 Hz (n=134 recordings)
Total recording duration: 64 h
Signal · Electrodes & live trace#
Live trace viewer — sub-S200306 · ses-01 · task-Rest · run-01
Showing one representative recording out of
45 subjects and 134 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.
No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.
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 |
Deception_data |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
20 |
Authors |
Makowski, Dominique, Pham, Tam, Lau, Zen Juen |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004511 · Makowski2023_Deceptioneegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004511(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Deception_data
- Study:
ds004511(OpenNeuro)- Author (year):
Makowski2023_Deception- Canonical:
—
Also importable as:
DS004511,Makowski2023_Deception.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
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/ds004511 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004511 DOI: https://doi.org/10.18112/openneuro.ds004511.v1.0.2 NEMAR citation count: 2
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: 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/ds004511").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 ds004511 to reproduce the tutorial on this dataset.
Citation
Makowski, Dominique, Pham, Tam, Lau, Zen Juen (20). Deception_data. 10.18112/openneuro.ds004511.v1.0.2
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004511.v1.0.2.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset