DS004554: eeg dataset, 16 subjects#
Forced Picture Naming Task
Citation: V. Volpert, B. Xu, A. Tchechmedjiev, S. Harispe, A. Aksenov, Q. Mesnildrey and A. Beuter (—). Forced Picture Naming Task. 10.18112/openneuro.ds004554.v1.0.4
16-participant EEG dataset — Forced Picture Naming Task.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS004554
dataset = DS004554(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS004554(cache_dir="./data", subject="01")
Advanced query
dataset = DS004554(
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{ds004554,
title = {Forced Picture Naming Task},
author = {V. Volpert and B. Xu and A. Tchechmedjiev and S. Harispe and A. Aksenov and Q. Mesnildrey and A. Beuter},
doi = {10.18112/openneuro.ds004554.v1.0.4},
url = {https://doi.org/10.18112/openneuro.ds004554.v1.0.4},
}
About This Dataset#
This is the preprocessed dataset used for study “Characterization of spatiotemporal dynamics in EEG data during picture naming with optical flow patterns”.
The Picture Naming Task study included sixteen native French-speaking men, ranging in age from 18 to 70 years old. The participants met the inclusion criteria, which required normal or corrected-to-normal vision and hearing, as well as right-handedness, as determined by a handedness questionnaire [Oldfield1971assessment]. Exclusion criteria were in place to ensure that participants had no history of neurological or psychiatric disorders, drug addiction, or head trauma. In total 20 subjects were included in the study. The four first subjects’ data was excluded due to hardware failure.
Participants were required to name the pictures shown on a screen. Each event (random pictures) has three phases: [-2s, 0s] is the baseline (pre-visual-stimulation); at time 0 picture is shown on screen; then [0s, 1.5s] post-stimulation phase; [1.5s, 3s], naming phase. Pictures used in the task were selected from the Snodgrass & Vanderwart black-and-white line drawing corpus [Snodgrass1980standardized]. “./code/experiment_schema.pdf” showed the task design.
Data pre-processing pipeline is illustrated in “./code/preprocess_pipeline.pdf”. In total, 270 trials each for the 16 subjects.
Cohort#
Dataset Statistics#
Channel counts: 99 ch (n=16 recordings)
Sampling frequencies: 1000.0 Hz (n=16 recordings)
Total recording duration: 1 min
Signal · Electrodes & live trace#
Live trace viewer — sub-S07 · task-picturenaming
Showing one representative recording out of
16 subjects and 16 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 · 96 sensors — 96 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 |
Forced Picture Naming Task |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
|
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds004554,
title = {Forced Picture Naming Task},
author = {V. Volpert and B. Xu and A. Tchechmedjiev and S. Harispe and A. Aksenov and Q. Mesnildrey and A. Beuter},
doi = {10.18112/openneuro.ds004554.v1.0.4},
url = {https://doi.org/10.18112/openneuro.ds004554.v1.0.4},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004554 · Volpert2023eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004554(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Forced Picture Naming Task
- Study:
ds004554(OpenNeuro)- Author (year):
Volpert2023- Canonical:
—
Also importable as:
DS004554,Volpert2023.Modality:
eeg. Subjects: 16; recordings: 16; 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/ds004554 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004554 DOI: https://doi.org/10.18112/openneuro.ds004554.v1.0.4 NEMAR citation count: 0
Examples
>>> from eegdash.dataset import DS004554 >>> dataset = DS004554(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/ds004554").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 ds004554 to reproduce the tutorial on this dataset.
Citation
V. Volpert, B. Xu, A. Tchechmedjiev, S. Harispe, A. Aksenov, … (n.d.). Forced Picture Naming Task. 10.18112/openneuro.ds004554.v1.0.4
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004554.v1.0.4.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset