DS007162#

Adaptive recruitment of cortex-wide recurrence for visual object recognition (EEG)

Access recordings and metadata through EEGDash.

Citation: [Unspecified1], [Unspecified2] (2026). Adaptive recruitment of cortex-wide recurrence for visual object recognition (EEG). 10.18112/openneuro.ds007162.v1.0.0

Modality: eeg Subjects: 34 Recordings: 590 License: CC0 Source: openneuro

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS007162

dataset = DS007162(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 = DS007162(cache_dir="./data", subject="01")

Advanced query

dataset = DS007162(
    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{ds007162,
  title = {Adaptive recruitment of cortex-wide recurrence for visual object recognition (EEG)},
  author = {[Unspecified1] and [Unspecified2]},
  doi = {10.18112/openneuro.ds007162.v1.0.0},
  url = {https://doi.org/10.18112/openneuro.ds007162.v1.0.0},
}

About This Dataset#

Dataset Description

This dataset contains the EEG data accompanying the study “Adaptive recruitment of cortex-wide recurrence for visual object recognition” (Link to preprint: https://www.biorxiv.org/content/10.1101/2025.10.17.682937v2). Please cite the above paper if you use this data.

Dataset Overview

34 participants, each with 1 session

Experimental Design

The EEG experiment used a stimulus set of 242 images (121 “challenge” and 121 “control” images) derived from comparisons between human behavioural performance and AlexNet.

Main task: Each trial consisted of a single image presented for 200 ms followed by a 100 ms blank. Trials were grouped into sequences of 14 images. At the end of each sequence, participants reported whether a paper clip appeared anywhere in that sequence.

Derivatives

The derivatives/ folder contains outputs from the decoding analyses, including time-resolved decoding accuracy matrices for object identity.

Dataset Information#

Dataset ID	`DS007162`
Title	Adaptive recruitment of cortex-wide recurrence for visual object recognition (EEG)
Year	2026
Authors	[Unspecified1], [Unspecified2]
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds007162.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: 34
Recordings: 590
Tasks: 1

Channels & sampling rate

Channels: 63
Sampling rate (Hz): 1000.0
Duration (hours): 0.0

API Reference#

Use the DS007162 class to access this dataset programmatically.

class eegdash.dataset.DS007162(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds007162. Modality: eeg. Subjects: 34; recordings: 69; 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/ds007162 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds007162

Examples

>>> from eegdash.dataset import DS007162
>>> dataset = DS007162(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