DS007406: eeg dataset, 10 subjects#

EEG dataset on consumer responses to extreme versus traditional marketing videos

Access recordings and metadata through EEGDash.

Citation: Allison Edit, Attila Pohlmann (2026). EEG dataset on consumer responses to extreme versus traditional marketing videos. 10.18112/openneuro.ds007406.v1.0.0

Modality: eeg Subjects: 10 Recordings: 10 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007406

dataset = DS007406(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS007406(cache_dir="./data", subject="01")

Advanced query

dataset = DS007406(
    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{ds007406,
  title = {EEG dataset on consumer responses to extreme versus traditional marketing videos},
  author = {Allison Edit and Attila Pohlmann},
  doi = {10.18112/openneuro.ds007406.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007406.v1.0.0},
}

About This Dataset#

This dataset comprises EEG recordings from ten participants exposed to six marketing video stimuli from three companies (Red Bull, GoPro, Columbia Sportswear), categorized as traditional product-focused advertisements versus “extreme” authentic documentary-style videos. Data were collected using a 14-channel EMOTIV EPOC X headset.

Dataset Information#

Dataset ID

DS007406

Title

EEG dataset on consumer responses to extreme versus traditional marketing videos

Author (year)

Edit2026

Canonical

Edit2024

Importable as

DS007406, Edit2026, Edit2024

Year

2026

Authors

Allison Edit, Attila Pohlmann

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds007406.v1.0.0

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds007406,
  title = {EEG dataset on consumer responses to extreme versus traditional marketing videos},
  author = {Allison Edit and Attila Pohlmann},
  doi = {10.18112/openneuro.ds007406.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007406.v1.0.0},
}

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: 10

  • Recordings: 10

  • Tasks: 1

Channels & sampling rate

  • Channels: 14

  • Sampling rate (Hz): 256.0

  • Duration (hours): 0.5000651041666667

Tags

  • Pathology: Healthy

  • Modality: Multisensory

  • Type: Affect

Files & format

  • Size on disk: 25.8 MB

  • File count: 10

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds007406.v1.0.0

Provenance

API Reference#

Use the DS007406 class to access this dataset programmatically.

class eegdash.dataset.DS007406(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

EEG dataset on consumer responses to extreme versus traditional marketing videos

Study:

ds007406 (OpenNeuro)

Author (year):

Edit2026

Canonical:

Edit2024

Also importable as: DS007406, Edit2026, Edit2024.

Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 10; recordings: 10; 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/ds007406 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007406 DOI: https://doi.org/10.18112/openneuro.ds007406.v1.0.0

Examples

>>> from eegdash.dataset import DS007406
>>> dataset = DS007406(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

See Also#