DS005291#

65 By ANT

Access recordings and metadata through EEGDash.

Citation: Zhao Xiangyue, Zhou Jingyao, Zhang Libo, Duan Haoqing, Wei Shiyu, Bi Yanzhi, Hu Li (2024). 65 By ANT. 10.18112/openneuro.ds005291.v1.0.0

Modality: eeg Subjects: 65 Recordings: 461 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005291

dataset = DS005291(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 = DS005291(cache_dir="./data", subject="01")

Advanced query

dataset = DS005291(
    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{ds005291,
  title = {65 By ANT},
  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.ds005291.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005291.v1.0.0},
}

About This Dataset#

1.Study introduction: In this experiment, participants were initially exposed to a series of laser stimulations of varying intensities. Researchers identified the energy intensity corresponding to an average rating of 7 from the participants. Subsequently, each participant underwent 30 laser stimuli and provided verbal pain ratings one by one. The pain ratings were on a scale where 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 underwent laser stimulation and subsequently verbally rated the intensity of pain. 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. 4.References and links: None 5.Comment: All laser markers are delayed by 100ms

Dataset Information#

Dataset ID	`DS005291`
Title	65 By ANT
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.ds005291.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: 65
Recordings: 461
Tasks: 1

Channels & sampling rate

Channels: 32
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS005291 class to access this dataset programmatically.

class eegdash.dataset.DS005291(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005291. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 65; recordings: 65; 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/ds005291 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005291

Examples

>>> from eegdash.dataset import DS005291
>>> dataset = DS005291(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