DS004603: eeg dataset, 37 subjects#

Visual Attribute-Specific Contextual Trajectory Paradigm

Access recordings and metadata through EEGDash.

Citation: Benjamin Lowe (ben.lowe@mq.edu.au), Jonathan Robinson (jonathan.robinson@monash.edu), Naohide Yamamoto (naohide.yamamoto@qut.edu.au), Hinze Hogendoorn (hinze.hogendoorn@qut.edu.au), Patrick Johnston (dr.pat.johnston@icloud.com) (2023). Visual Attribute-Specific Contextual Trajectory Paradigm. 10.18112/openneuro.ds004603.v1.1.0

Modality: eeg Subjects: 37 Recordings: 37 License: CC0 Source: openneuro Citations: 1.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS004603

dataset = DS004603(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 = DS004603(cache_dir="./data", subject="01")

Advanced query

dataset = DS004603(
    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{ds004603,
  title = {Visual Attribute-Specific Contextual Trajectory Paradigm},
  author = {Benjamin Lowe (ben.lowe@mq.edu.au) and Jonathan Robinson (jonathan.robinson@monash.edu) and Naohide Yamamoto (naohide.yamamoto@qut.edu.au) and Hinze Hogendoorn (hinze.hogendoorn@qut.edu.au) and Patrick Johnston (dr.pat.johnston@icloud.com)},
  doi = {10.18112/openneuro.ds004603.v1.1.0},
  url = {https://doi.org/10.18112/openneuro.ds004603.v1.1.0},
}

About This Dataset#

These data were recorded from 37 subjects using the following exclusion criteria: Normal, or correct to normal, vision; no history of neurological disorder; and less than 35 years of age. Subjects completed a novel, visual contextual trajectory paradigm (CTP) wherein the onset of a bound stimulus violated an established trajectory in terms of its brightness, size, or orientation. No attribute was violated during control trials. Full method details can be read within the following published paper: https://doi.org/10.1016/j.cortex.2023.08.004 Analysis code is available at: benjaminglowe/attribute-specific-prediction-error-analysis-code Please email ben.lowe@mq.edu.au if you have any further questions.

Dataset Information#

Dataset ID	`DS004603`
Title	Visual Attribute-Specific Contextual Trajectory Paradigm
Author (year)	`Lowe2023`
Canonical	—
Importable as	`DS004603`, `Lowe2023`
Year	2023
Authors	Benjamin Lowe (ben.lowe@mq.edu.au), Jonathan Robinson (jonathan.robinson@monash.edu), Naohide Yamamoto (naohide.yamamoto@qut.edu.au), Hinze Hogendoorn (hinze.hogendoorn@qut.edu.au), Patrick Johnston (dr.pat.johnston@icloud.com)
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds004603.v1.1.0
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: 37
Recordings: 37
Tasks: 1

Channels & sampling rate

Channels: 65
Sampling rate (Hz): 1024.0
Duration (hours): 30.653045518663195

Electrode Layout#

Electrode layout — EEG · 64 sensors — 64 channels

Dataset Statistics#

Age distribution (n=37, range 17–30 yr)

15202530

Sex distribution

30

7

Female Male Total: 37

Channel counts: 65 ch (n=37 recordings)

Sampling frequencies: 1024.0 Hz (n=37 recordings)

Total recording duration: 30 h

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 DS004603 class to access this dataset programmatically.

class eegdash.dataset.DS004603(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

Visual Attribute-Specific Contextual Trajectory Paradigm

Study:: ds004603 (OpenNeuro)
Author (year):: Lowe2023
Canonical:: —

Also importable as: DS004603, Lowe2023.

Modality: eeg. Subjects: 37; recordings: 37; 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/ds004603 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds004603 DOI: https://doi.org/10.18112/openneuro.ds004603.v1.1.0 NEMAR citation count: 1

Examples

>>> from eegdash.dataset import DS004603
>>> dataset = DS004603(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)