DS006394#

Electrophysiological markers of surprise-induced failures of visual and auditory awareness

Access recordings and metadata through EEGDash.

Citation: En-Lin Leong, Yun Da Chua, Takashi Obana, Christopher L. Asplund (2025). Electrophysiological markers of surprise-induced failures of visual and auditory awareness. 10.18112/openneuro.ds006394.v1.0.3

Modality: eeg Subjects: 33 Recordings: 368 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS006394

dataset = DS006394(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 = DS006394(cache_dir="./data", subject="01")

Advanced query

dataset = DS006394(
    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{ds006394,
  title = {Electrophysiological markers of surprise-induced failures of visual and auditory awareness},
  author = {En-Lin Leong and Yun Da Chua and Takashi Obana and Christopher L. Asplund},
  doi = {10.18112/openneuro.ds006394.v1.0.3},
  url = {https://doi.org/10.18112/openneuro.ds006394.v1.0.3},
}

About This Dataset#

This is the dataset for Leong et al. (in prep). 33 participants completed both a visual and auditory surprise task in counterbalanced order. Methodological details are contained in the manuscript.

Certain participants were excluded at various stages of the analyses. Their data and event lists are included up to the stage of processing that their data reached.

Due to incorrect settings specific to OpenBCI GUI v5.0.1, indicated EEG values are 24 times larger than what they should be. The units (also specified in the channels.tsv files) are thus in microvolts / 24.

Dataset Information#

Dataset ID	`DS006394`
Title	Electrophysiological markers of surprise-induced failures of visual and auditory awareness
Year	2025
Authors	En-Lin Leong, Yun Da Chua, Takashi Obana, Christopher L. Asplund
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds006394.v1.0.3
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: 33
Recordings: 368
Tasks: 2

Channels & sampling rate

Channels: 16
Sampling rate (Hz): 125.0
Duration (hours): 0.0

API Reference#

Use the DS006394 class to access this dataset programmatically.

class eegdash.dataset.DS006394(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds006394. Modality: eeg; Experiment type: Attention; Subject type: Healthy. Subjects: 33; recordings: 60; tasks: 2.

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/ds006394 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds006394

Examples

>>> from eegdash.dataset import DS006394
>>> dataset = DS006394(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