EEGdash›NeMAR›ON007454
Iss. 7454 · 42 subjects · 42 recordings · CC0
Dataset Brief · A common neural mechanism underlies experiences of passage of…

ON007454: eeg dataset, 42 subjects#

A common neural mechanism underlies experiences of passage of time

Access recordings and metadata through EEGDash.

Citation: [Unspecified] (2019). A common neural mechanism underlies experiences of passage of time. 10.82901/nemar.on007454

Modality: eeg Subjects: 42 Recordings: 42 License: CC0 Source: nemar

Metadata: Good (80%)

42-participant EEG dataset — A common neural mechanism underlies experiences of passage of time.

Data & curation [Unspecified]
Senior author [Unspecified]
Year 2019 · Distributed via NeMAR
BIDS 1.7.0Task · Starfield
Layer 01Study
What was asked
Hypothesis, independent & dependent variables, paradigm, cohort, and the editorial caveats around what the recordings can and cannot answer.
Layer 02Signal · BIDS
What was recorded
Sidecars, channels & electrodes, coordinate system, event semantics, and quality stats from the NEMAR pipeline when available.
Layer 03Training · ML
What you can train on
Recommended access modes — MNE Raw, braindecode windows, PyTorch DataLoader — plus the targets the metadata makes addressable.
§ 01Access · Get started

Quickstart#

Install

pip install eegdash

Access the data

from eegdash.dataset import ON007454

dataset = ON007454(cache_dir="./data")
# Get the raw object of the first recording
raw = dataset.datasets[0].raw
print(raw.info)

Filter by subject

dataset = ON007454(cache_dir="./data", subject="01")

Advanced query

dataset = ON007454(
    cache_dir="./data",
    query={"subject": {"$in": ["01", "02"]}},
)

Iterate recordings

for rec in dataset:
    print(rec.subject, rec.raw.info['sfreq'])

If you use this dataset in your research, please cite the original authors.

BibTeX

@dataset{on007454,
  title = {A common neural mechanism underlies experiences of passage of time},
  author = {[Unspecified]},
  doi = {10.82901/nemar.on007454},
  url = {https://doi.org/10.82901/nemar.on007454},
}
§ 02Study · The README

About This Dataset#

This repository contains the BIDS-formatted dataset generated from EEG and behavioral data.

bids_dataset

DOI

Raw data for the study ‘A common neural mechanism underlies experiences of passage of time’

├── sub-XXX
│   ├── eeg
│   └── sub-XXX_scans.tsv
├── dataset_description.json
├── participants.json
├── participants.tsv
├── README.md
└── CHANGES.txt
├── sourcedata
│   └── sub-XXX

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.01896Pernet, 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

§ 03Cohort · Participants

Cohort#

§ 04Signal · Electrodes & trace

Signal · Electrodes & live trace#

Fig. 01 Signal & montage — ch · EEG · Varies · 42 subjects, 42 recordings

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

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.

NEMAR pipeline statistics — ON007454
HED event descriptors word cloud HED event descriptors word cloud — ON007454
§ 05Manifest · BIDS tree

Manifest#

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.

Recordings—
Files—
Subjects—
Modalities—
Click to load file structure…
Full dataset metadata table

Dataset ID

ON007454

Title

A common neural mechanism underlies experiences of passage of time

Author (year)

—

Canonical

—

Importable as

ON007454

Year

2019

Authors

[Unspecified]

License

CC0

Citation / DOI

10.82901/nemar.on007454

Source links

OpenNeuro | NeMAR | Source URL
Copy-paste BibTeX
@dataset{on007454,
  title = {A common neural mechanism underlies experiences of passage of time},
  author = {[Unspecified]},
  doi = {10.82901/nemar.on007454},
  url = {https://doi.org/10.82901/nemar.on007454},
}
§ 06API · Programmatic access

API Reference#

Signature
eegdash.dataset
class
eegdash.dataset.ON007454(cache_dir, query=None, s3_bucket=None, **kwargs)
Bases: EEGDashDataset
Author (year)—
Canonical—
Importable asON007454
Sourceeegdash/dataset/registry.py · [source ↗]
class eegdash.dataset.ON007454(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

A common neural mechanism underlies experiences of passage of time

Study:

on007454 (NeMAR)

Author (year):

—

Canonical:

—

Also importable as: ON007454.

Modality: eeg; Subject type: Unknown. Subjects: 42; recordings: 42; 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/on007454 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=on007454 DOI: https://doi.org/10.82901/nemar.on007454

Examples

>>> from eegdash.dataset import ON007454
>>> dataset = ON007454(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)
Parameters:

  • path (str) –

    Directory in which subdirectories are created to store

    -raw.fif | -epo.fif and .json files to.

  • overwrite (bool) – Whether to delete old subdirectories that will be saved to in this call.

  • offset (int) – If provided, the integer is added to the id of the dataset in the concat. This is useful in the setting of very large datasets, where one dataset has to be processed and saved at a time to account for its original position.

Access modesMNE → braindecode → PyTorch → ML
.rawMNE Raw object — standard tools (filter, epoch, ICA, plot_psd).mne
BaseConcatDatasetEach record is a lazy BaseDataset from braindecode — windowed via create_windows_from_events.braindecode
DataLoaderWraps the windowed dataset into a PyTorch DataLoader; supports parallel workers and on-the-fly augmentations.pytorch
Zarr cacheOptional braindecode Zarr mirror for fast resume; persisted to cache_dir.zarr
Hugging FaceNo per-dataset mirror published yet — browse the EEGDash org listing for sibling datasets. See the datasets loader API.huggingface
Croissant 1.0Machine-readable JSON-LD descriptor — ON007454.croissant.json (MLCommons schema, ingestible by PyTorch / TensorFlow / JAX).mlcommons
Examples using EEGDashcurated · start here
Find datasets with the EEGDash APIQuery the catalogue, filter by task or modality, list candidates.Load one EEG recordingResolve a single record to an MNE Raw with channels and events.EEG recording to PyTorch DataLoaderWrap braindecode windows in a DataLoader for model training.Preprocess EEG and create windowsFilter, resample, epoch — and persist the windowed dataset.Save and reload prepared dataCache a windowed dataset to disk and reattach it without recompute.Download a dataset locallyPrefetch BIDS files to a local cache and validate the layout.

Swap any load_dataset(...) call for on007454 to reproduce the tutorial on this dataset.

Citation

[Unspecified] (2019). A common neural mechanism underlies experiences of passage of time. 10.82901/nemar.on007454

Provenance

¹Contributed to nemar in BIDS format.

²Curated & ingested by the EEGDash catalog; see CITATION.cff for canonical reference.

³Persistent identifier: 10.82901/nemar.on007454.

BIDS
BIDS 1.7.0
Sidecars
not yet probed
Provenance
CC0 · 10.82901/nemar.on007454
Machine-readable
schema.org/Dataset · Croissant
Mirrors
OpenNeuro · NEMAR · HF org

See Also#