DS005397#

Affordances of stairs

Access recordings and metadata through EEGDash.

Citation: Christopher Hilton, Lilian Befort, Ronja Brinkmann, Matthias Ballestrem, Joerg Fingerhut, Klaus Gramann (2024). Affordances of stairs. 10.18112/openneuro.ds005397.v1.0.4

Modality: eeg Subjects: 26 Recordings: 188 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005397

dataset = DS005397(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 = DS005397(cache_dir="./data", subject="01")

Advanced query

dataset = DS005397(
    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{ds005397,
  title = {Affordances of stairs},
  author = {Christopher Hilton and Lilian Befort and Ronja Brinkmann and Matthias Ballestrem and Joerg Fingerhut and Klaus Gramann},
  doi = {10.18112/openneuro.ds005397.v1.0.4},
  url = {https://doi.org/10.18112/openneuro.ds005397.v1.0.4},
}

About This Dataset#

An EEG dataset and behavioural response data for a task that required participants to view images of scenes and rate their aesthetic properties (beauty, complexity, interestingness), or rate their appropriateness for either a reading activity, or a social activity.

You can also find the behavioural data already extracted from the EEG events for convenience, and the full stimuli set with identifiable file names.

For detailed information about the methods and an analysis of the data please see the published article: https://doi.org/10.1016/j.jenvp.2025.102528

Contact: c.hilton@tu-berlin.de in case of questions.

Dataset Information#

Dataset ID	`DS005397`
Title	Affordances of stairs
Year	2024
Authors	Christopher Hilton, Lilian Befort, Ronja Brinkmann, Matthias Ballestrem, Joerg Fingerhut, Klaus Gramann
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005397.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: 26
Recordings: 188
Tasks: 1

Channels & sampling rate

Channels: 64
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS005397 class to access this dataset programmatically.

class eegdash.dataset.DS005397(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005397. Modality: eeg; Experiment type: Affect; Subject type: Healthy. Subjects: 26; recordings: 26; 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/ds005397 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005397

Examples

>>> from eegdash.dataset import DS005397
>>> dataset = DS005397(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