DS004588#

Neuma

Access recordings and metadata through EEGDash.

Citation: Kostas Georgiadis, Fotis P. Kalaganis, Kyriakos Riskos, Eleytheria Matta, Vangelis P. Oikonomou, Yfantidou Ioanna, Dimitris Chantziaras, Kyriakos Pantouvakis, Spiros Nikolopoulos, Nikos A. Laskaris, Ioannis Kompatsiaris (2023). Neuma. 10.18112/openneuro.ds004588.v1.2.0

Modality: eeg Subjects: 42 Recordings: 216 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004588

dataset = DS004588(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 = DS004588(cache_dir="./data", subject="01")

Advanced query

dataset = DS004588(
    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{ds004588,
  title = {Neuma},
  author = {Kostas Georgiadis and Fotis P. Kalaganis and Kyriakos Riskos and Eleytheria Matta and Vangelis P. Oikonomou and Yfantidou Ioanna and Dimitris Chantziaras and Kyriakos Pantouvakis and Spiros Nikolopoulos and Nikos A. Laskaris and Ioannis Kompatsiaris},
  doi = {10.18112/openneuro.ds004588.v1.2.0},
  url = {https://doi.org/10.18112/openneuro.ds004588.v1.2.0},
}

About This Dataset#

A novel multimodal Neuromarketing dataset that encompasses the data from 42 individuals who participated in an advertising brochure-browsing scenario is introduced here. In more detail, participants were exposed to a series of supermarket brochures (containing various products) and instructed to select the products they intended to buy. The data collected for each individual executing this protocol included: (i) encephalographic (EEG) recordings, (ii) eye tracking (ET) recordings, (iii) questionnaire responses (demographic, profiling and product related questions), and (iv) computer mouse data.

The preprocessed version of this dataset can be found here: https://figshare.com/articles/dataset/NeuMa_PreProcessed_A_multimodal_Neuromarketing_dataset/22117124

Dataset Information#

Dataset ID	`DS004588`
Title	Neuma
Year	2023
Authors	Kostas Georgiadis, Fotis P. Kalaganis, Kyriakos Riskos, Eleytheria Matta, Vangelis P. Oikonomou, Yfantidou Ioanna, Dimitris Chantziaras, Kyriakos Pantouvakis, Spiros Nikolopoulos, Nikos A. Laskaris, Ioannis Kompatsiaris
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004588.v1.2.0
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: 42
Recordings: 216
Tasks: 1

Channels & sampling rate

Channels: 24
Sampling rate (Hz): 300.0
Duration (hours): 0.0

API Reference#

Use the DS004588 class to access this dataset programmatically.

class eegdash.dataset.DS004588(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004588. Modality: eeg; Experiment type: Decision-making; Subject type: Healthy. Subjects: 42; recordings: 42; 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/ds004588 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004588

Examples

>>> from eegdash.dataset import DS004588
>>> dataset = DS004588(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