EEGdash›OpenNeuro›DS006963
Iss. 6963 · 32 subjects · 32 recordings · CC0
Dataset Brief · Motor Control Processes Moderate Visual Working Memory Gating…

DS006963: eeg dataset, 32 subjects#

Motor Control Processes Moderate Visual Working Memory Gating Dataset

Citation: Şahcan Özdemir, Eren Günseli, Daniel Schneider (2025). Motor Control Processes Moderate Visual Working Memory Gating Dataset. 10.18112/openneuro.ds006963.v1.0.1

32-participant EEG dataset — Motor Control Processes Moderate Visual Working Memory Gating Dataset.

Data & curation Şahcan Özdemir · Eren Günseli · Daniel Schneider
Year 2025 · Distributed via OpenNeuro
EEG · 64 ch1000 HzBIDS v1.10.1Task · VisuomotorDelayedMatchToSampleWiHealthyVisualMemory
Layer 01Study
What was asked
Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.
Layer 02Signal · BIDS
What was recorded
Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.
Layer 03Training · ML
What you can train on
Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.
§ 01Access · Get started

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006963

dataset = DS006963(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS006963(cache_dir="./data", subject="01")

Advanced query

dataset = DS006963(
    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{ds006963,
  title = {Motor Control Processes Moderate Visual Working Memory Gating Dataset},
  author = {Şahcan Özdemir and Eren Günseli and Daniel Schneider},
  doi = {10.18112/openneuro.ds006963.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds006963.v1.0.1},
}
§ 02Study · The README

About This Dataset#

This dataset accompanies the paper “Motor Control Processes Moderate

Working Memory Gating,” published in The Journal of Neuroscience. It contains the raw EEG recordings (not preprocessed) from the study, as well as each participant’s behavioral data within the EEG dataset struct (labeled as EEG.behaviordata).

Mean age of subjects is 23.7 (sd=2.9). For any of your inquiries,

please reach out to the corresponding author: oezdemir@ifado.de You can find the explanation of triggers at"task-VisuomotorDelayedMatchToSampleWithInterference_events.json”. For any of your inquiries, “task-VisuomotorDelayedMatchToSampleWithInterference_events.json”.

While using the dataset, please cite: Özdemir, Ş., Günseli, E., & Schneider, D. (2025). Motor control processes moderate visual working memory gating. The Journal of Neuroscience, 45(47), e0673252025. https://doi.org/10.1523/ JNEUROSCI.0673-25.2025 To reach the analysis codes, please visit the OSF project (https://osf.io/7fve8).

Participants’ subject numbers were randomized to ensure anonymity and do not reflect the order of data collection. The dataset includes 32 participants in total. Two participants were excluded from all analyses due to misunderstanding of the

View full README

While using the dataset, please cite: Özdemir, Ş., Günseli, E., & Schneider, D. (2025). Motor control processes moderate visual working memory gating. The Journal of Neuroscience, 45(47), e0673252025. https://doi.org/10.1523/ JNEUROSCI.0673-25.2025 To reach the analysis codes, please visit the OSF project (https://osf.io/7fve8).

Participants’ subject numbers were randomized to ensure anonymity and do not reflect the order of data collection. The dataset includes 32 participants in total. Two participants were excluded from all analyses due to misunderstanding of the task rules (one participant didn’t follow the interference task, an the other participant tried to use the response knobs during the target presentation). One participant was included only in the behavioral analysis because of abnormal EEG data, and one participant was excluded based on predefined exclusion criteria. However these excluded participants are shared within this dataset to further ensure transparency. All cases are documented in the relevant notes and the participant info list “participants.tsv”.

For detailed methodological information, please refer to the paper or the associated OSF project (https://osf.io/7fve8). A brief summary of the experimental procedure is provided here.

The experiment used a 2×2 within-subject design with four conditions: same-hand motor interference, different-hand motor interference, same-hand visuomotor interference, and different-hand visuomotor interference.

Participants also completed 240 baseline trials with no interference. The experiment included 10 blocks, each containing 120 trials.

Each trial began with a colored square or diamond presented for 500 ms. After a 2900 ms delay, participants reported the target color using a color wheel controlled by the left or right knob. The shape of the stimulus indicated which hand to use, and this mapping was counterbalanced across participants.

Participants had 4000 ms to respond, and each trial ended with an inter-trial interval between 800 and 1400 ms.

In two-thirds of the trials, an interference task occurred during the delay. At 900 ms, a left- or right-pointing triangle appeared, and participants pressed the knob with the corresponding hand. In the motor interference condition, these triangles were gray. In the visuomotor interference condition, the triangles were colored, with their hue shifted 60–90 degrees from the target color, introducing visual interference. In the remaining no-interference trials, a gray up- or down-pointing triangle appeared, and participants made no response until the color wheel appeared.

§ 03Cohort · Participants

Cohort#

Dataset Statistics#

Channel counts: 64 ch (n=32 recordings)

Sampling frequencies: 1000.0 Hz (n=32 recordings)

Total recording duration: 85 h

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage 64 ch · EEG · 1000 Hz · 32 subjects, 32 recordings
Live trace viewer — sub-021 · task-VisuomotorDelayedMatchToSampleWithInterference

Showing one representative recording out of 32 subjects and 32 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.

Electrode layout — EEG · 64 sensors — 64 channels

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.

NEMAR pipeline statistics — DS006963
HED event descriptors word cloud HED event descriptors word cloud — DS006963
§ 05Manifest · BIDS tree

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.

Recordings—
Files—
Subjects—
Modalities—
Click to load file structure…
Full dataset metadata table

Dataset ID

DS006963

Title

Motor Control Processes Moderate Visual Working Memory Gating Dataset

Author (year)

Ozdemir2025

Canonical

—

Importable as

DS006963, Ozdemir2025

Year

2025

Authors

Şahcan Özdemir, Eren Günseli, Daniel Schneider

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds006963.v1.0.1

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds006963,
  title = {Motor Control Processes Moderate Visual Working Memory Gating Dataset},
  author = {Şahcan Özdemir and Eren Günseli and Daniel Schneider},
  doi = {10.18112/openneuro.ds006963.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds006963.v1.0.1},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.DS006963(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)Ozdemir2025
Canonical—
Importable asDS006963 · Ozdemir2025
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.DS006963(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Motor Control Processes Moderate Visual Working Memory Gating Dataset

Study:

ds006963 (OpenNeuro)

Author (year):

Ozdemir2025

Canonical:

—

Also importable as: DS006963, Ozdemir2025.

Modality: eeg; Experiment type: Memory; Subject type: Healthy. Subjects: 32; recordings: 32; 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/ds006963 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006963 DOI: https://doi.org/10.18112/openneuro.ds006963.v1.0.1

Examples

>>> from eegdash.dataset import DS006963
>>> dataset = DS006963(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.

Access modesMNE → braindecode → PyTorch → ML
.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne
BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode
DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch
Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr
Hugging FacePre-bundled mirror at EEGDash/ds006963 · pull with datasets.load_dataset("EEGDash/ds006963").huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — DS006963.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons
Examples using EEGDashcurated · start here
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 ds006963 to reproduce the tutorial on this dataset.

Citation

Şahcan Özdemir, Eren Günseli, Daniel Schneider (2025). Motor Control Processes Moderate Visual Working Memory Gating Dataset. 10.18112/openneuro.ds006963.v1.0.1

Provenance

¹Contributed to openneuro in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.18112/openneuro.ds006963.v1.0.1.

BIDS
BIDS v1.10.1
Sidecars
events · channels · electrodes · coordsystem · eeg.json
Machine-readable
schema.org/Dataset · Croissant

See Also#