DS004200: eeg dataset, 20 subjects#

Temporal Scaling

Access recordings and metadata through EEGDash.

Citation: Cameron D. Hassall, Jack Harley, Nils Kolling, Laurence T. Hunt (2022). Temporal Scaling. 10.18112/openneuro.ds004200.v1.0.1

Modality: eeg Subjects: 20 Recordings: 20 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004200

dataset = DS004200(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 = DS004200(cache_dir="./data", subject="01")

Advanced query

dataset = DS004200(
    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{ds004200,
  title = {Temporal Scaling},
  author = {Cameron D. Hassall and Jack Harley and Nils Kolling and Laurence T. Hunt},
  doi = {10.18112/openneuro.ds004200.v1.0.1},
  url = {https://doi.org/10.18112/openneuro.ds004200.v1.0.1},
}

About This Dataset#

Temporal Scaling

Twenty participants learned three temporal intervals. There were two subtasks, randomly interleaved. In the Production task, participants produced either a short, medium, or long temporal interval. In the Perception task, participants judged a computer-produced interval as correct or incorrect (again, for a short, medium, or long temporal interval). In both tasks participants received visual feedback (a checkmark or x). Preprint: https://doi.org/10.1101/2020.12.11.421180

Dataset Information#

Dataset ID	`DS004200`
Title	Temporal Scaling
Author (year)	`Hassall2022_Temporal`
Canonical	—
Importable as	`DS004200`, `Hassall2022_Temporal`
Year	2022
Authors	Cameron D. Hassall, Jack Harley, Nils Kolling, Laurence T. Hunt
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004200.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: 20
Recordings: 20
Tasks: 1

Channels & sampling rate

Channels: 37
Sampling rate (Hz): 1000.0
Duration (hours): 14.122722222222222

Electrode Layout#

Electrode layout — EEG · 37 sensors — 37 channels

Dataset Statistics#

Age distribution (n=20, range 20–77 yr)

2025303540455060657075

Sex distribution

13

7

Female Male Total: 20

Channel counts: 37 ch (n=20 recordings)

Sampling frequencies: 1000.0 Hz (n=20 recordings)

Total recording duration: 14 h 7 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 DS004200 class to access this dataset programmatically.

class eegdash.dataset.DS004200(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Temporal Scaling

Study:: ds004200 (OpenNeuro)
Author (year):: Hassall2022_Temporal
Canonical:: —

Also importable as: DS004200, Hassall2022_Temporal.

Modality: eeg. Subjects: 20; recordings: 20; 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/ds004200 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004200 DOI: https://doi.org/10.18112/openneuro.ds004200.v1.0.1 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004200
>>> dataset = DS004200(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)