DS004554#

Forced Picture Naming Task

Access recordings and metadata through EEGDash.

Citation: V. Volpert, B. Xu, A. Tchechmedjiev, S. Harispe, A. Aksenov, Q. Mesnildrey and A. Beuter (2023). Forced Picture Naming Task. 10.18112/openneuro.ds004554.v1.0.4

Modality: eeg Subjects: 16 Recordings: 101 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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.

Dataset Information#

Dataset ID	`DS004554`
Title	Forced Picture Naming Task
Year	2023
Authors	Volpert, B. Xu, A. Tchechmedjiev, S. Harispe, A. Aksenov, Q. Mesnildrey and A. Beuter
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004554.v1.0.4
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: 16
Recordings: 101
Tasks: 1

Channels & sampling rate

Channels: 99
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS004554 class to access this dataset programmatically.

class eegdash.dataset.DS004554(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004554. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. 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

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/ds004554 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004554

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, 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