DS004505: eeg dataset, 25 subjects#
Real World Table Tennis
Citation: Amanda Studnicki, Daniel P. Ferris (—). Real World Table Tennis. 10.18112/openneuro.ds004505.v1.0.4
25-participant EEG dataset — Real World Table Tennis.
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.
Cohort#
Dataset Statistics#
Age distribution (n=25, range 18–30 yr, mean 22.0 yr · sex per subject not reported)
Sex composition
Channel counts (ch)
Sampling frequencies: 250.0 Hz (n=25 recordings)
Total recording duration: 30 h
Signal · Electrodes & live trace#
Live trace viewer — sub-13 · task-TableTennis
Showing one representative recording out of
25 subjects and 25 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 · 120 sensors — 120 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.
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 |
Real World Table Tennis |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Amanda Studnicki, Daniel P. Ferris |
License |
CC0 |
Citation / DOI |
|
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},
}
API Reference#
eegdash.datasetEEGDashDatasetDS004505 · Studnicki2023eegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS004505(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Real World Table Tennis
- Study:
ds004505(OpenNeuro)- Author (year):
Studnicki2023- Canonical:
—
Also importable as:
DS004505,Studnicki2023.Modality:
eeg. 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
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/ds004505 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004505 DOI: https://doi.org/10.18112/openneuro.ds004505.v1.0.4 NEMAR citation count: 5
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: 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/ds004505").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 ds004505 to reproduce the tutorial on this dataset.
Citation
Amanda Studnicki, Daniel P. Ferris (n.d.). Real World Table Tennis. 10.18112/openneuro.ds004505.v1.0.4
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds004505.v1.0.4.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset