ON007327: eeg dataset, 35 subjects#
Basketball Motor Imagery EEG Dataset
Access recordings and metadata through EEGDash.
Citation: Jiahui Han, Jiaqi Wang, Lingrong Jia, Ming Tang, School of Physics and Electronic Technology, Liaoning Normal University, Institute of Psychological and Brain Sciences, Liaoning Normal University (—). Basketball Motor Imagery EEG Dataset. 10.82901/nemar.on007327
Modality: eeg Subjects: 35 Recordings: 105 License: CC0 Source: nemar
Metadata: Complete (100%)
35-participant EEG dataset — Basketball Motor Imagery EEG Dataset.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON007327
dataset = ON007327(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON007327(cache_dir="./data", subject="01")
Advanced query
dataset = ON007327(
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{on007327,
title = {Basketball Motor Imagery EEG Dataset},
author = {Jiahui Han and Jiaqi Wang and Lingrong Jia and Ming Tang and School of Physics and Electronic Technology, Liaoning Normal University and Institute of Psychological and Brain Sciences, Liaoning Normal University},
doi = {10.82901/nemar.on007327},
url = {https://doi.org/10.82901/nemar.on007327},
}
About This Dataset#
“Basketball Motor Observation and Motor Imagery EEG Dataset”
“=====================================” “” “This dataset contains EEG recordings from 35 healthy participants performing 3 basketball-related motor observation and motor imagery tasks.” “” “Tasks:” “1. dribble - Single-handed dribbling task with 5 conditions” “2. ballpass - Two-handed ball passing task with 4 conditions” “3. passing - Two-person passing task with 4 combined conditions (MOMO/MOMI/MIMO/MIMI) and 4 PLV conditions (observe/imagine opponent→self/self→opponent)” “” “Data includes:” “- Raw EEG data” “- Preprocessed data” “- PLV functional connectivity results” “” “Recorded with ANT Neuro system (64 channels, 1000Hz sampling rate).” “” “For more details, see the dataset_description.json and participants.json files.” “” “This dataset follows the BIDS 1.8.0 standard.”
Cohort#
Dataset Statistics#
Age distribution by gender (n=35, range 17–26 yr, mean 21.5 yr)
Sex composition
Channel counts: 64 ch (n=105 recordings)
Sampling frequencies: 1000.0 Hz (n=105 recordings)
Total recording duration: 45 h
Signal · Electrodes & live trace#
Live trace viewer — sub-031 · task-ballpass
Showing one representative recording out of
35 subjects and 105 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 · 63 sensors — 63 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 |
Basketball Motor Imagery EEG Dataset |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Jiahui Han, Jiaqi Wang, Lingrong Jia, Ming Tang, School of Physics and Electronic Technology, Liaoning Normal University, Institute of Psychological and Brain Sciences, Liaoning Normal University |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{on007327,
title = {Basketball Motor Imagery EEG Dataset},
author = {Jiahui Han and Jiaqi Wang and Lingrong Jia and Ming Tang and School of Physics and Electronic Technology, Liaoning Normal University and Institute of Psychological and Brain Sciences, Liaoning Normal University},
doi = {10.82901/nemar.on007327},
url = {https://doi.org/10.82901/nemar.on007327},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON007327(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
Basketball Motor Imagery EEG Dataset
- Study:
on007327(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
ON007327.Modality:
eeg; Subject type:Unknown. Subjects: 35; recordings: 105; tasks: 3.- 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/on007327 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on007327 DOI: https://doi.org/10.82901/nemar.on007327
Examples
>>> from eegdash.dataset import ON007327 >>> dataset = ON007327(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.pytorch
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 on007327 to reproduce the tutorial on this dataset.
Citation
Jiahui Han, Jiaqi Wang, Lingrong Jia, Ming Tang, School of Physics and Electronic Technology, Liaoning Normal University, … (n.d.). Basketball Motor Imagery EEG Dataset. 10.82901/nemar.on007327
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on007327.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset