DS005293: eeg dataset, 95 subjects#
95 By BP
Citation: Zhao Xiangyue, Zhou Jingyao, Zhang Libo, Duan Haoqing, Wei Shiyu, Bi Yanzhi, Hu Li (—). 95 By BP. 10.18112/openneuro.ds005293.v1.0.0
95-participant EEG dataset — 95 By BP.
Quickstart#
Install
pip install eegdash
Access the data
from eegdash.dataset import DS005293
dataset = DS005293(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)
Filter by subject
dataset = DS005293(cache_dir="./data", subject="01")
Advanced query
dataset = DS005293(
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{ds005293,
title = {95 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.ds005293.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds005293.v1.0.0},
}
About This Dataset#
1.Study introduction:
In this experiment, the intensity of laser stimulation varied individually based on participants’pain thresholds. Prior to the formal commencement of the experiment, participants underwent a series of stimuli of increasing intensity, incrementally rising from low to high in 0.25J steps until reaching the maximum tolerable intensity for each individual. Participants were instructed to verbally report the perceived pain intensity of each laser stimulation using a numerical rating scale (NRS) ranging from 0 (no sensation) to 10 (the maximum tolerable level of pain), with 4 indicating the pain perception threshold akin to a pricking sensation. In this study, each participant received four levels of stimulation intensity, corresponding to ratings of 2, 4, 6, and 8 on the NRS (E1: 2.0 ± 0.2 J; E2: 2.7 ± 0.3 J; E3: 3.4 ± 0.3 J; E4: 4.1 ± 0.4 J).
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):
The participants were instructed to relax and sit comfortably on a chair, focusing their attention on the sensation of laser stimulation. The experimental environment was quiet, with a constant room temperature, and no unrelated individuals were present. Both the participants and the experimenter wore protective goggles. The experimental design employed a two-factor repeated measures within-subject design, with 4 levels of stimulation intensity crossed with 2 levels of stimulation location (left hand dorsum and right hand dorsum), resulting in a total of 8 conditions (stimulation locations: left hand dorsum and right hand dorsum). There were 10 trials for each condition, totaling 80 trials. Participants received pain stimulation and provided ratings for each trial individually.
Cohort#
Dataset Statistics#
Age distribution (n=95, range 18–26 yr, mean 21.6 yr · sex per subject not reported)
Sex composition
Channel counts: 60 ch (n=570 recordings)
Sampling frequencies: 1000.0 Hz (n=570 recordings)
Total recording duration: 234 h
Signal · Electrodes & live trace#
Live trace viewer — sub-021 · ses-4 · task-95ByBP
Showing one representative recording out of
95 subjects and 570 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 · 60 sensors — 60 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 |
95 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{ds005293,
title = {95 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.ds005293.v1.0.0},
url = {https://doi.org/10.18112/openneuro.ds005293.v1.0.0},
}
API Reference#
eegdash.datasetEEGDashDatasetDS005293 · Xiangyue2024_95_BPeegdash/dataset/registry.py · [source ↗]- class eegdash.dataset.DS005293(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#
95 By BP
- Study:
ds005293(OpenNeuro)- Author (year):
Xiangyue2024_95_BP- Canonical:
—
Also importable as:
DS005293,Xiangyue2024_95_BP.Modality:
eeg; Experiment type:Perception; Subject type:Healthy. Subjects: 95; recordings: 570; 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/ds005293 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005293 DOI: https://doi.org/10.18112/openneuro.ds005293.v1.0.0
Examples
>>> from eegdash.dataset import DS005293 >>> dataset = DS005293(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/ds005293").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 ds005293 to reproduce the tutorial on this dataset.
Citation
Zhao Xiangyue, Zhou Jingyao, Zhang Libo, Duan Haoqing, Wei Shiyu, … (n.d.). 95 By BP. 10.18112/openneuro.ds005293.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.ds005293.v1.0.0.
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See Also#
eegdash.dataset.EEGDashDataseteegdash.dataset