DS004505#

Real World Table Tennis

Access recordings and metadata through EEGDash.

Citation: Amanda Studnicki, Daniel P. Ferris (2023). Real World Table Tennis. 10.18112/openneuro.ds004505.v1.0.4

Modality: eeg Subjects: 25 Recordings: 230 License: CC0 Source: openneuro Citations: 5.0

Metadata: Complete (100%)

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004505

dataset = DS004505(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = DS004505(cache_dir="./data", subject="01")

Advanced query

dataset = DS004505(
    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{ds004505,
  title = {Real World Table Tennis},
  author = {Amanda Studnicki and Daniel P. Ferris},
  doi = {10.18112/openneuro.ds004505.v1.0.4},
  url = {https://doi.org/10.18112/openneuro.ds004505.v1.0.4},
}

About This Dataset#

Our dataset contains high-density, dual-layer electroencephalography (EEG), neck electromyography (EMG), inertial measurement unit (IMU) acceleration, T1 structural MR images, and video data from 25 participants playing real-world table tennis. Participants played 60 minutes of table tennis (in total) with a ball machine and a human player, with an additional 10 minutes of standing baseline. For 17 of the participants, we also include video data of all trials. The Adobe Premiere project files (linked to each video) have the timing of hit events marked.

Data in the main subject folders have been processed. We include the ICA decomposition and dipole model in EEG.etc. The components retained in our analyses are shown in EEG.etc.KeepComponents. The raw data can be found in the sourcedata folder.

Please refer to our publication for more details.

Dataset Information#

Dataset ID

DS004505

Title

Real World Table Tennis

Year

2023

Authors

Amanda Studnicki, Daniel P. Ferris

License

CC0

Citation / DOI

doi:10.18112/openneuro.ds004505.v1.0.4

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{ds004505,
  title = {Real World Table Tennis},
  author = {Amanda Studnicki and Daniel P. Ferris},
  doi = {10.18112/openneuro.ds004505.v1.0.4},
  url = {https://doi.org/10.18112/openneuro.ds004505.v1.0.4},
}

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: 25

  • Recordings: 230

  • Tasks: 1

Channels & sampling rate

  • Channels: 120 (25), 313 (13), 270 (4), 299 (2), 312 (2), 340, 326, 327, 303

  • Sampling rate (Hz): 250.0

  • Duration (hours): 0.0

Tags

  • Pathology: Not specified

  • Modality: —

  • Type: —

Files & format

  • Size on disk: 34.6 GB

  • File count: 230

  • Format: BIDS

License & citation

  • License: CC0

  • DOI: doi:10.18112/openneuro.ds004505.v1.0.4

Provenance

API Reference#

Use the DS004505 class to access this dataset programmatically.

class eegdash.dataset.DS004505(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds004505. Modality: eeg; Experiment type: Motor; Subject type: Healthy. Subjects: 25; recordings: 25; 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/ds004505 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004505

Examples

>>> from eegdash.dataset import DS004505
>>> dataset = DS004505(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

See Also#