DS005692#

SpinalExpect_NonInvasive

Access recordings and metadata through EEGDash.

Citation: Max-Philipp Stenner, Cindy Marquez Nossa, Tino Zaehle, Elena Azanon, Hans-Jochen Heinze, Matthias Deliano, Lars Buentjen (2024). SpinalExpect_NonInvasive. 10.18112/openneuro.ds005692.v1.0.0

Modality: eeg Subjects: 30 Recordings: 417 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005692

dataset = DS005692(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 = DS005692(cache_dir="./data", subject="01")

Advanced query

dataset = DS005692(
    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{ds005692,
  title = {SpinalExpect_NonInvasive},
  author = {Max-Philipp Stenner and Cindy Marquez Nossa and Tino Zaehle and Elena Azanon and Hans-Jochen Heinze and Matthias Deliano and Lars Buentjen},
  doi = {10.18112/openneuro.ds005692.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds005692.v1.0.0},
}

About This Dataset#

Contact person: Max-Philipp Stenner, email: max-philipp.stenner@med.ovgu.de or max-philipp.stenner@lin-magdeburg.de, ORCID: 0000-0002-3694-1887 Brief overview of the tasks in the experiment: The goal was to reveal whether temporal expectation influences initial sensory processing in the human spinal cord. In each trial, subjects (n=30, healthy, young) received at least one electric median nerve stimulation on the left wrist. Their task was to silently count in how many (rare) trials in each block (~5 min) there were two median nerve stimuli in rapid succession (80 ms ISI), and to report that number at the end of each block. In each trial, the median nerve stimulation was preceded by an auditory cue. In alternating blocks, the time interval between the auditory cue and the (first) median nerve stimulation was either fixed at 1100 ms, or varied randomly (uniform distribution) between 100 and 2100 ms. Each subject (except for sub-24) completed two days of testing. Whether a session began with a constant-interval block or variable-interval block was counterbalanced across subjects. Montage: Adapted from/extended beyond Chander, B. S., Deliano, M., Azañón, E., Büntjen, L., & Stenner, M. P. (2022). Non-invasive recording of high-frequency signals from the human spinal cord. NeuroImage, 253, 119050, as follows. Electrodes were placed in three rings around the neck. “Sr” in the channel names stands for “spinal ring”. There was a caudal, middle, and cranial ring of electrodes. The middle ring was connecting a point above the spinous process of the sixth cervical vertebra (SrC6), and a point above the thyroid cartilage (“SrF”). Between these two electrodes, the remaining six electrodes for the middle ring were evenly spaced around the neck. “L” in channel names means left, “R” means right, “B” means “back”, “F” means front, and “M” means middle. For the caudal and cranial rings, electrodes were placed approximately 2 cm below (“_b”) and 2 cm above (“_a”) the corresponding electrodes of the middle ring, respectively. Events: There are three events of interest. “S1” is a keypress of the experimenter, to advance the task upon breaks. “S1” can therefore be used to identify the start of each new block. “S70” corresponds to the auditory cue. “S130” corresponds to median nerve stimulation. “S99” is a “placeholder” that corresponds to no stimulus at all. The placeholder was used to enable “double stimuli”, i.e., a rapid succession of two median nerve stimulations with an ISI of 80 ms. Most trials follow a sequence of “S70” - “S130” - “S99”. Deviant trials have “S70” - “S130” - “S130”.

Dataset Information#

Dataset ID	`DS005692`
Title	SpinalExpect_NonInvasive
Year	2024
Authors	Max-Philipp Stenner, Cindy Marquez Nossa, Tino Zaehle, Elena Azanon, Hans-Jochen Heinze, Matthias Deliano, Lars Buentjen
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005692.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: 30
Recordings: 417
Tasks: 1

Channels & sampling rate

Channels: 24
Sampling rate (Hz): 5000.0
Duration (hours): 0.0

API Reference#

Use the DS005692 class to access this dataset programmatically.

class eegdash.dataset.DS005692(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005692. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 30; recordings: 59; 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/ds005692 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005692

Examples

>>> from eegdash.dataset import DS005692
>>> dataset = DS005692(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