DS006563#

Dimension-based attention modulates early visual processing

Access recordings and metadata through EEGDash.

Citation: Klaus Gramann, Thomas Töllner, Hermann J. Müller (2025). Dimension-based attention modulates early visual processing. 10.18112/openneuro.ds006563.v1.0.0

Modality: eeg Subjects: 12 Recordings: 66 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006563

dataset = DS006563(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 = DS006563(cache_dir="./data", subject="01")

Advanced query

dataset = DS006563(
    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{ds006563,
  title = {Dimension-based attention modulates early visual processing},
  author = {Klaus Gramann, Thomas Töllner, Hermann J. Müller},
  doi = {10.18112/openneuro.ds006563.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006563.v1.0.0},
}

About This Dataset#

Overview

This dataset was originally published in Gramann, K., Töllner, T. and Müller, H.J. (2010), Dimension-based attention modulates early visual processing. Psychophysiology, 47: 968-978. https://doi.org/10.1111/j.1469-8986.2010.00998.x It was subsequently used to investigate automatic labeling of independent components in ICA and is referred to as the “Cue” dataset: Frølich, L., Andersen, T.S. and Mørup, M. (2015), Multi-class classification of ICS of EEG. Psychophysiol, 52: 32-45. https://doi.org/10.1111/psyp.12290 “64 scalp channels “referenced to Cz and re-referenced off-line to linked mastoids” from 12 subjects during a visual task (Gramann et al., 2010). ICA was performed with the implementation of the ICA infomax algorithm in the Brain Vision Analyzer software from Brain Products GmbH.2 The data sets we had access to were between 56 and 66 min long for the different subjects.” After contacting the above authors, Laura Frølich provided a copy of the data. With Klaus Gramann’s permission, this was converted to BIDS format by Austin J. Brockmeier and Carlos H. Mendoza-Cardenas. Continuous EEG from 12 with ICA weights.

Subjects

“Twelve observers took part in the Experiment (2 female; age range 21–25 years). All were right-handed, had normal or corrected-to-normal vision, and reported no history of neurological disorder. Observers were either paid or received course credit for participating. All observers provided written informed consent, and the experimental procedure was approved by the ethics committee of the Department of Psychology, University of Munich, in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki).”

Expert-annotated Independent Components (ICs)

The expert-annotated labels of the ICs can be found in the field expert_ica_labels, and the class names in ica_classes. expert_ica_labels(i) is the Matlab-index of ica_classes for the i-th IC. ICs can be computed using the fields data, icasphere, and icaweights (e.g., icaact = icaweights * icasphere * data).

Details related to access to the data

CC-BY

Contact persons: klaus.gramann@tu-berlin.de https://orcid.org/0000-0003-2673-1832 ajbrock@udel.edu https://orcid.org/0000-0002-7293-8140

Dataset Information#

Dataset ID	`DS006563`
Title	Dimension-based attention modulates early visual processing
Year	2025
Authors	Klaus Gramann, Thomas Töllner, Hermann J. Müller
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006563.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: 12
Recordings: 66
Tasks: 1

Channels & sampling rate

Channels: 64 (12), 63 (12)
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS006563 class to access this dataset programmatically.

class eegdash.dataset.DS006563(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006563. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 12; recordings: 12; 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/ds006563 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006563

Examples

>>> from eegdash.dataset import DS006563
>>> dataset = DS006563(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