DS006563: eeg dataset, 12 subjects#
Dimension-based attention modulates early visual processing
Citation: Klaus Gramann, Thomas Töllner, Hermann J. Müller (2010). Dimension-based attention modulates early visual processing. 10.18112/openneuro.ds006563.v1.0.0
12-participant EEG dataset — Dimension-based attention modulates early visual processing.
Quickstart#
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)
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'])
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#
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.
Overview
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 inica_classes.expert_ica_labels(i)is the Matlab-index ofica_classesfor the i-th IC. ICs can be computed using the fieldsdata,icasphere, andicaweights(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
Cohort#
Dataset Statistics#
Channel counts: 64 ch (n=12 recordings)
Sampling frequencies: 500.0 Hz (n=12 recordings)
Total recording duration: 12 h 38 min
Signal · Electrodes & live trace#
Live trace viewer — sub-S12 · task-DimensionDiscrimination
Showing one representative recording out of
12 subjects and 12 recordings in this dataset.
Browse the full set on OpenNeuro;
drop any other _eeg.{set,edf,bdf,vhdr} file onto the
viewer (or pass ?eeg=<url>) to inspect it.
No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.
NEMAR Processing Statistics#
The plots below are generated by NEMAR’s automated EEG pipeline. The histogram shows pipeline success for data cleaning and ICA decomposition, the percentage of data frames and EEG channels retained after artefact removal, line noise per channel (RMS, dB), and the age/gender distribution of participants.
HED event descriptors word cloud
Manifest#
File Explorer#
Browse the BIDS file structure of this dataset. Records are fetched on demand from the EEGDash catalog the first time you open the explorer.
Full dataset metadata table
Dataset ID |
|
Title |
Dimension-based attention modulates early visual processing |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
2010 |
Authors |
Klaus Gramann, Thomas Töllner, Hermann J. Müller |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste 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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS006563 · Gramann2025eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS006563(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Dimension-based attention modulates early visual processing
- Study:
ds006563(OpenNeuro)- Author (year):
Gramann2025- Canonical:
—
Also importable as:
DS006563,Gramann2025.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
Notes
Each item is a recording; recording-level metadata are available via
dataset.description.querysupports MongoDB-style filters on fields inALLOWED_QUERY_FIELDSand 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 DOI: https://doi.org/10.18112/openneuro.ds006563.v1.0.0
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: str, overwrite: bool = False, offset: int = 0)[source]#
Save datasets to files by creating one subdirectory for each dataset:
path/ 0/ 0-raw.fif | 0-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw) 1/ 1-raw.fif | 1-epo.fif description.json raw_preproc_kwargs.json (if raws were preprocessed) window_kwargs.json (if this is a windowed dataset) window_preproc_kwargs.json (if windows were preprocessed) target_name.json (if target_name is not None and dataset is raw)
- Parameters:
path (str) –
- Directory in which subdirectories are created to store
-raw.fif | -epo.fif and .json files to.
overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.
offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.
BaseDataset from braindecode — windowed via create_windows_from_events.braindecodeDataLoader; supports parallel workers and on-the-fly augmentations.pytorchdatasets.load_dataset("EEGDash/ds006563").huggingface
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.
Load one EEG recordingResolve a single record to an MNE Raw with channels and events.
EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.
Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.
Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.
Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.Swap any load_dataset(...) call for ds006563 to reproduce the tutorial on this dataset.
Citation
Klaus Gramann, Thomas Töllner, Hermann J. Müller (2010). Dimension-based attention modulates early visual processing. 10.18112/openneuro.ds006563.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds006563.v1.0.0.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset