DS005106#

200 Objects Infants EEG

Access recordings and metadata through EEGDash.

Citation: Tijl Grootswagers, Genevieve Quek, Zhen Zeng, Manuel Varlet (2024). 200 Objects Infants EEG. 10.18112/openneuro.ds005106.v1.5.0

Modality: eeg Subjects: 42 Recordings: 133 License: CC0 Source: openneuro Citations: 0.0

Metadata: Complete (100%)

Quickstart#

Get Started

Install

pip install eegdash

Access the data

from eegdash.dataset import DS005106

dataset = DS005106(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 = DS005106(cache_dir="./data", subject="01")

Advanced query

dataset = DS005106(
    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{ds005106,
  title = {200 Objects Infants EEG},
  author = {Tijl Grootswagers and Genevieve Quek and Zhen Zeng and Manuel Varlet},
  doi = {10.18112/openneuro.ds005106.v1.5.0},
  url = {https://doi.org/10.18112/openneuro.ds005106.v1.5.0},
}

About This Dataset#

Data and code for the paper: Tijl Grootswagers, Genevieve Quek, Zhen Zeng, & Manuel Varlet. 2025. “Human Infant EEG Recordings for 200 Object Images Presented in Rapid Visual Streams.” Scientific Data. https://doi.org/10.1038/s41597-025-04744-z

See the linked paper for details.

The “code” directory contains all the code to reproduce the figures in the paper. It requires fieldtrip and cosmomvpa, change the paths to these toolboxes at the top of each script (or remove the lines and add them to the path manually).

Then run the scripts to reproduce each step reported in the paper: 1. run_preprocessing.m (preprocess and epoch data) 2. run_rsa.m (makes the individual RDMs) 3. stats_rsa.m (computes the RSA correlations) 4. plot_design.m (produces Figure 1 in the paper) 5. plot_peaks.m (produces Figure 2 in the paper) 6. plot_rsa.m (produces Figure 3 in the paper)

Each script can also run standalone, as intermediate results are saved in the derivates folder

Dataset Information#

Dataset ID	`DS005106`
Title	200 Objects Infants EEG
Year	2024
Authors	Tijl Grootswagers, Genevieve Quek, Zhen Zeng, Manuel Varlet
License	CC0
Citation / DOI	doi:10.18112/openneuro.ds005106.v1.5.0
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: 42
Recordings: 133
Tasks: 1

Channels & sampling rate

Channels: 33
Sampling rate (Hz): 500.0
Duration (hours): 0.0

API Reference#

Use the DS005106 class to access this dataset programmatically.

class eegdash.dataset.DS005106(cache_dir: str, query: dict | None = None, s3_bucket: str | None = None, **kwargs)[source]#

Bases: EEGDashDataset

OpenNeuro dataset ds005106. Modality: eeg; Experiment type: Attention; Subject type: Healthy. 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/ds005106 NeMAR dataset: https://nemar.org/dataexplorer/detail?dataset_id=ds005106

Examples

>>> from eegdash.dataset import DS005106
>>> dataset = DS005106(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