DS005307: eeg dataset, 7 subjects#

Laser-evoked potentials in the human spinal cord and cortex

Access recordings and metadata through EEGDash.

Citation: Birgit Nierula, Tilman Stephani, Emma Bailey, Merve Kaptan, Lisa-Marie Pohle, Ulrike Horn, Andre Mouraux, Burkhard Maess, Arno Villringer, Gabriel Curio, Vadim Nikulin, Falk Eippert (2024). Laser-evoked potentials in the human spinal cord and cortex. 10.18112/openneuro.ds005307.v1.0.1

Modality: eeg Subjects: 7 Recordings: 73 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005307

dataset = DS005307(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 = DS005307(cache_dir="./data", subject="01")

Advanced query

dataset = DS005307(
    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{ds005307,
  title = {Laser-evoked potentials in the human spinal cord and cortex},
  author = {Birgit Nierula and Tilman Stephani and Emma Bailey and Merve Kaptan and Lisa-Marie Pohle and Ulrike Horn and Andre Mouraux and Burkhard Maess and Arno Villringer and Gabriel Curio and Vadim Nikulin and Falk Eippert},
  doi = {10.18112/openneuro.ds005307.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds005307.v1.0.1},
}

About This Dataset#

References

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896). https://doi.org/10.21105/joss.01896 Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8

Dataset Information#

Dataset ID	`DS005307`
Title	Laser-evoked potentials in the human spinal cord and cortex
Author (year)	`Nierula2024`
Canonical	—
Importable as	`DS005307`, `Nierula2024`
Year	2024
Authors	Birgit Nierula, Tilman Stephani, Emma Bailey, Merve Kaptan, Lisa-Marie Pohle, Ulrike Horn, Andre Mouraux, Burkhard Maess, Arno Villringer, Gabriel Curio, Vadim Nikulin, Falk Eippert
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005307.v1.0.1
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: 7
Recordings: 73
Tasks: 1

Channels & sampling rate

Channels: 77 (50), 109 (18), 110 (5)
Sampling rate (Hz): 10000.0
Duration (hours): 1.5768097222222224

Electrode Layout#

No scalp electrode layout is currently indexed for this dataset. Once the eegdash montage registry ingests it, the interactive viewer will appear here automatically.

Dataset Statistics#

Channel counts (ch)

77109110

Sampling frequencies: 10000.0 Hz (n=73 recordings)

Total recording duration: 1 h 34 min

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

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.

Files: —

Size: —

Subjects: —

Click to load file structure…

API Reference#

Use the DS005307 class to access this dataset programmatically.

class eegdash.dataset.DS005307(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Laser-evoked potentials in the human spinal cord and cortex

Study:: ds005307 (OpenNeuro)
Author (year):: Nierula2024
Canonical:: —

Also importable as: DS005307, Nierula2024.

Modality: eeg; Experiment type: Perception; Subject type: Healthy. Subjects: 7; recordings: 73; 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/ds005307 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005307 DOI: https://doi.org/10.18112/openneuro.ds005307.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS005307
>>> dataset = DS005307(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)