DS005292#

142 by Biosemi

Access recordings and metadata through EEGDash.

Citation: Zhao Xiangyue, Zhou Jingyao, Zhang Libo, Duan Haoqing, Wei Shiyu, Bi Yanzhi, Hu Li (2024). 142 by Biosemi. 10.18112/openneuro.ds005292.v1.0.0

Modality: eeg Subjects: 142 Recordings: 2136 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005292

dataset = DS005292(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Query & Filter

Filter by subject

dataset = DS005292(cache_dir="./data", subject="01")

Advanced query

dataset = DS005292(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

Cite This Dataset

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{ds005292,
  title = {142 by Biosemi},
  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.ds005292.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005292.v1.0.0},
}

About This Dataset#

1.Study introduction: In this experiment, the first 135 participants received fixed-intensity pain stimuli at 3J (low pain) and 3.5J (high pain), while participants 136-142 received fixed-intensity pain stimuli at 3.5J (low pain) and 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.

Dataset Information#

Dataset ID	`DS005292`
Title	142 by Biosemi
Year	2024
Authors	Zhao Xiangyue, Zhou Jingyao, Zhang Libo, Duan Haoqing, Wei Shiyu, Bi Yanzhi, Hu Li
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005292.v1.0.0
Source links	OpenNeuro \| NeMAR \| Source URL

Found an issue with this dataset?

If you encounter any problems with this dataset (missing files, incorrect metadata, loading errors, etc.), please let us know!

Report an Issue on GitHub

Technical Details#

Subjects & recordings

Subjects: 142
Recordings: 2136
Tasks: 1

Channels & sampling rate

Channels: 64 (828), 256 (24)
Sampling rate (Hz): 1024.0 (840), 2048.0 (12)
Duration (hours): 0.0

API Reference#

Use the DS005292 class to access this dataset programmatically.

class eegdash.dataset.DS005292(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005292. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 142; recordings: 426; 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

query#

Merged query with the dataset filter applied.

Type:: dict

records#

Metadata records used to build the dataset, if pre-fetched.

Type:: list[dict] | None

Notes

Each item is a recording; recording-level metadata are available via dataset.description. query supports MongoDB-style filters on fields in ALLOWED_QUERY_FIELDS and is combined with the dataset filter. Dataset-specific caveats are not provided in the summary metadata.

References

OpenNeuro dataset: https://openneuro.org/datasets/ds005292 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005292

Examples

>>> from eegdash.dataset import DS005292
>>> dataset = DS005292(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, overwrite=False)[source]#

Save the dataset to disk.

Parameters:

path (str or Path) – Destination file path.
overwrite (bool, default False) – If True, overwrite existing file.

Return type:

None