DS005285#

29 By ANT

Access recordings and metadata through EEGDash.

Citation: Zhao Xiangyue, Zhou Jingyao, Zhang Libo, Duan Haoqing, Wei Shiyu, Bi Yanzhi, Hu Li (2024). 29 By ANT. 10.18112/openneuro.ds005285.v1.0.0

Modality: eeg Subjects: 29 Recordings: 818 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005285

dataset = DS005285(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 = DS005285(cache_dir="./data", subject="01")

Advanced query

dataset = DS005285(
    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{ds005285,
  title = {29 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.ds005285.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005285.v1.0.0},
}

About This Dataset#

1.Study introduction: Firstly, participants underwent a series of laser stimulations of varying intensities. The experimenters determined the energy intensities corresponding to average scores of 4 and 7 points among the participants. Subsequently, each participant received a fixed-intensity laser stimulation approximately every 20 seconds, constituting one block of 40 trials, with half being high intensity and half low intensity. There were a total of 4 blocks, resulting in 160 stimulations in total. During this period, participants provided pain ratings ranging from 0 to 10. A rating of 0 indicated no sensation, 4 denoted the onset of pain perception, 6 represented moderate pain, 8 indicated severe pain, and 10 signified intolerable pain. 2.Participant task information(description of the experiment): Participants received laser stimulation and used a computer mouse to click on the appropriate position on the screen, corresponding to a scale of 0 to 10. 3.Participant instructions(as exact as possible): Participants were instructed to focus their attention on the laser stimuli, keep their eyes open, and fixate their gaze on the cross displayed on the screen. Following each laser stimulation, there was a 3-second pause. Subsequently, participants used the computer screen and keyboard to assess the intensity of pain within a 5-second time window. The subsequent trial commenced randomly within 1-3 seconds after the rating was provided. 4.References and links: Bi Y, Liu X, Zhao X, et al. Enhancing pain modulation: the efficacy of synchronous combination of virtual reality and transcutaneous electrical nerve stimulation. General Psychiatry 2023;36:e101164. doi:10.1136/gpsych-2023-101164. 5.Comments: In the raw data, “32” is used to represent “s32”,”64” is used to represent “s64”.

Dataset Information#

Dataset ID	`DS005285`
Title	29 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.ds005285.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: 29
Recordings: 818
Tasks: 1

Channels & sampling rate

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

API Reference#

Use the DS005285 class to access this dataset programmatically.

class eegdash.dataset.DS005285(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005285. Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 29; recordings: 116; 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/ds005285 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005285

Examples

>>> from eegdash.dataset import DS005285
>>> dataset = DS005285(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