DS004347#

Symmetry perception and affective responses: a combined EEG/EMG study

Access recordings and metadata through EEGDash.

Citation: Makin, A. D. J, Wilton, M. M, Pecchinenda, A., Bertamini, M. (2022). Symmetry perception and affective responses: a combined EEG/EMG study. 10.18112/openneuro.ds004347.v1.0.0

Modality: eeg Subjects: 24 Recordings: 173 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004347

dataset = DS004347(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 = DS004347(cache_dir="./data", subject="01")

Advanced query

dataset = DS004347(
    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{ds004347,
  title = {Symmetry perception and affective responses: a combined EEG/EMG study},
  author = {Makin, A. D. J and Wilton, M. M and Pecchinenda, A. and Bertamini, M.},
  doi = {10.18112/openneuro.ds004347.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds004347.v1.0.0},
}

About This Dataset#

SPN1 Experiment 1 Project 1. After stimulus offset, participants reported whether patterns were regular or random.

For full catalogue, see https://osf.io/2sncj/

Dataset Information#

Dataset ID	`DS004347`
Title	Symmetry perception and affective responses: a combined EEG/EMG study
Year	2022
Authors	Makin, A. D. J, Wilton, M. M, Pecchinenda, A., Bertamini, M.
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004347.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: 24
Recordings: 173
Tasks: 1

Channels & sampling rate

Channels: 72 (24), 64 (24)
Sampling rate (Hz): 512.0
Duration (hours): 0.0

API Reference#

Use the DS004347 class to access this dataset programmatically.

class eegdash.dataset.DS004347(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004347. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 24; recordings: 24; 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/ds004347 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004347

Examples

>>> from eegdash.dataset import DS004347
>>> dataset = DS004347(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