DS006963#

Motor Control Processes Moderate Visual Working Memory Gating Dataset

Access recordings and metadata through EEGDash.

Citation: Şahcan Özdemir, Eren Günseli, Daniel Schneider (2025). Motor Control Processes Moderate Visual Working Memory Gating Dataset. 10.18112/openneuro.ds006963.v1.0.0

Modality: eeg Subjects: 32 Recordings: 197 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

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)

Query & Filter

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'])

Cite This Dataset

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.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds006963.v1.0.0},
}

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”.

View full README

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 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.

Dataset Information#

Dataset ID	`DS006963`
Title	Motor Control Processes Moderate Visual Working Memory Gating Dataset
Year	2025
Authors	Şahcan Özdemir, Eren Günseli, Daniel Schneider
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006963.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: 32
Recordings: 197
Tasks: 1

Channels & sampling rate

Channels: 64
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS006963 class to access this dataset programmatically.

class eegdash.dataset.DS006963(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006963. 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

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, 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