ON005289: eeg dataset, 39 subjects#
39 By BP
Access recordings and metadata through EEGDash.
Citation: Zhao Xiangyue, Zhou Jingyao, Zhang Libo, Duan Haoqing, Wei Shiyu, Bi Yanzhi, Hu Li (2021). 39 By BP. 10.82901/nemar.on005289
Modality: eeg Subjects: 39 Recordings: 195 License: CC0 Source: nemar
Metadata: Complete (100%)
39-participant EEG dataset — 39 By BP.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import ON005289
dataset = ON005289(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = ON005289(cache_dir="./data", subject="01")
Advanced query
dataset = ON005289(
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{on005289,
title = {39 By BP},
author = {Zhao Xiangyue and Zhou Jingyao and Zhang Libo and Duan Haoqing and Wei Shiyu and Bi Yanzhi and Hu Li},
doi = {10.82901/nemar.on005289},
url = {https://doi.org/10.82901/nemar.on005289},
}
About This Dataset#
1.Study introduction:
In this experiment, participants were initially subjected to a series of laser stimuli of varying intensities. Researchers determined the energy intensity corresponding to an average rating of 7 from the participants. Subsequently, each participant received 10 laser stimulations and was prompted to rate pain intensity (ranging from no sensation to the worst pain imaginable) and unpleasantness (ranging from no unpleasantness to the most unpleasant) on a numeric rating scale (NRS) from 0 to 10. Participants indicated their chosen scores by clicking on the corresponding position on a screen, and scores were recorded, potentially including decimal values, to accommodate for precision in pain assessment.
2.Participant task information(description of the experiment):
Participants were administered a laser stimulation and then provided scores by clicking on the corresponding position along the 0 to 10 axis using a mouse. 3.Participant instructions(as exact as possible):
Participants were instructed to focus their attention on the laser stimulation, keep their eyes open, and fixate their gaze on a crosshair displayed on the screen. Following the presentation of each laser stimulation, there was a 5-second pause. Subsequently, participants assessed the intensity of pain. The subsequent trials commenced randomly 5 seconds after providing the rating. 4.References and links:
Lu, X., Yao, X., Thompson, W. F., & Hu, L. (2021). Movement-induced hypoalgesia: behavioral characteristics and neural mechanisms. Annals of the New York Academy of Sciences, 1497, 39�C56. https://doi.org/10.1111/nyas.14587 5.Comments:
The age information of the subjects is missing.
Cohort#
Dataset Statistics#
Sex composition
Channel counts: 64 ch (n=195 recordings)
Sampling frequencies: 1000.0 Hz (n=195 recordings)
Total recording duration: 16 h 33 min
Signal · Electrodes & live trace#
Live trace viewer — sub-037 · ses-3 · task-39ByBP
Showing one representative recording out of
39 subjects and 195 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 |
39 By BP |
Author (year) |
— |
Canonical |
— |
Importable as |
|
Year |
2021 |
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{on005289,
title = {39 By BP},
author = {Zhao Xiangyue and Zhou Jingyao and Zhang Libo and Duan Haoqing and Wei Shiyu and Bi Yanzhi and Hu Li},
doi = {10.82901/nemar.on005289},
url = {https://doi.org/10.82901/nemar.on005289},
}
API Reference#
eegdash.datasetEEGDashDataset- class eegdash.dataset.ON005289(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
39 By BP
- Study:
on005289(NeMAR)- Author (year):
—
- Canonical:
—
Also importable as:
ON005289.Modality:
eeg; Subject type:Unknown. Subjects: 39; recordings: 195; 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/on005289 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on005289 DOI: https://doi.org/10.82901/nemar.on005289
Examples
>>> from eegdash.dataset import ON005289 >>> dataset = ON005289(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 on005289 to reproduce the tutorial on this dataset.
Citation
Zhao Xiangyue, Zhou Jingyao, Zhang Libo, Duan Haoqing, Wei Shiyu, … (2021). 39 By BP. 10.82901/nemar.on005289
Provenance
¹Contributed to nemar in BIDS format.
²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.
³Persistent identifier: 10.82901/nemar.on005289.
See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset