DS005280: eeg dataset, 223 subjects#
223 By BP
Citation: Zhao Xiangyue, Zhou Jingyao, Zhang Libo, Duan Haoqing, Wei Shiyu, Bi Yanzhi, Hu Li (—). 223 By BP. 10.18112/openneuro.ds005280.v1.0.0
223-participant EEG dataset — 223 By BP.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS005280
dataset = DS005280(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS005280(cache_dir="./data", subject="01")
Advanced query
dataset = DS005280(
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{ds005280,
title = {223 By BP},
author = {Zhao Xiangyue and Zhou Jingyao and Zhang Libo and Duan Haoqing and Wei Shiyu and Bi Yanzhi and Hu Li},
doi = {10.18112/openneuro.ds005280.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds005280.v1.0.0},
}
About This Dataset#
1.Study introduction:
In this experiment, participants received fixed-intensity pain stimuli at 3J / 3.5J (low pain) and 3.5J / 4J (high pain). Each participant underwent stimulation in 3 blocks, with each block comprising 10 stimuli, totaling 30 stimuli. High and low pain stimuli were evenly distributed within each block. After each stimulation, participants provided pain ratings individually. Pain ratings were as follows: 0 indicated no sensation at all, 4 indicated the onset of pain, 6 represented moderate pain, 8 indicated severe pain, and 10 denoted unbearable pain.
2.Participant task information(description of the experiment):
Participants received laser stimulation and subsequently provided pain intensity ratings one by one. 3.Participant instructions(as exact as possible):
Participants were instructed to focus on the laser stimulation, keep their eyes open, and fix their gaze on the crosshairs displayed on the screen. After each laser stimulation, there is a five-second pause. Participants then rated the intensity of the pain. Subsequent trials began at random 5 seconds after the score was provided.
Cohort#
Dataset Statistics#
Age distribution (n=223, range 16–32 yr, mean 20.8 yr · sex per subject not reported)
Sex composition
Channel counts: 64 ch (n=669 recordings)
Sampling frequencies: 1000.0 Hz (n=669 recordings)
Total recording duration: 98 h
Signal · Electrodes & live trace#
Live trace viewer — sub-213 · ses-3 · task-223ByBP
Showing one representative recording out of
223 subjects and 669 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.
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 |
223 By BP |
Author (year) |
|
Canonical |
— |
Importable as |
|
Year |
— |
Authors |
Zhao Xiangyue, Zhou Jingyao, Zhang Libo, Duan Haoqing, Wei Shiyu, Bi Yanzhi, Hu Li |
License |
CC0 |
Citation / DOI |
|
Source links |
OpenNeuro | NeMAR | Source URL |
Copy-paste BibTeX
@dataset{ds005280,
title = {223 By BP},
author = {Zhao Xiangyue and Zhou Jingyao and Zhang Libo and Duan Haoqing and Wei Shiyu and Bi Yanzhi and Hu Li},
doi = {10.18112/openneuro.ds005280.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds005280.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005280 · Xiangyue2024_223_BPeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005280(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
223 By BP
- Study:
ds005280(OpenNeuro)- Author (year):
Xiangyue2024_223_BP- Canonical:
—
Also importable as:
DS005280,Xiangyue2024_223_BP.Modality:
eeg; Experiment type:Perception; Subject type:Healthy. Subjects: 223; recordings: 669; 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/ds005280 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005280 DOI: https://doi.org/10.18112/openneuro.ds005280.v1.0.0
Examples
>>> from eegdash.dataset import DS005280 >>> dataset = DS005280(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/ds005280").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 ds005280 to reproduce the tutorial on this dataset.
Citation
Zhao Xiangyue, Zhou Jingyao, Zhang Libo, Duan Haoqing, Wei Shiyu, … (n.d.). 223 By BP. 10.18112/openneuro.ds005280.v1.0.0
Provenance
¹Contributed to openneuro in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.18112/openneuro.ds005280.v1.0.0.
Related & sibling datasets
+ 1 more — see See Also below →
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset