EEGDash/ds002718

Face processing EEG dataset for EEGLAB

This repository hosts the complete scientific files of NEMAR on002718, snapshot v1.0.0. It includes every subject and recording, the BIDS sidecars, and the source's sourcedata/ and code/ files where present. All source files were verified against NEMAR's manifest checksums before upload. Only DataLad control files and source .gitattributes files are omitted because Hugging Face manages repository storage. The original README is preserved as SOURCE_README.md.

This dataset is the EEG and structural MRI portion of a multi-subject, multi-modal face processing study (originally ds000117), repackaged in EEGLAB format with augmented event logs and HED annotation. Eighteen participants viewed famous, unfamiliar, and scrambled faces while making symmetry judgments. The original study had two sessions (fMRI and simultaneous MEG/EEG); only the MEEG session's EEG data and the structural MRI are included here, while MEG and fMRI data are excluded. Minimal preprocessing, including EEG channel extraction, event correction, and resampling to 250 Hz, yields a lightweight version suitable for tutorials and EEG source localization.

Load with the BIDS builder

These examples require datasets PR #9011 until its BIDS builder is released:

pip install "datasets[bids] @ git+https://github.com/bruAristimunha/datasets@feat/bids-biosignal" nibabel

from datasets import load_dataset


dataset = load_dataset("EEGDash/ds002718", split="train", datatypes="eeg")
row = dataset[0]
recording = row["biosignal"]
print(recording)
samples = recording.get_data(start=0, stop=100)
print(row["metadata"])

There are 18 subjects and 18 recordings in the complete mirror. The example may select one subject; all subjects are stored here. Accessing a recording caches its subject/session/datatype folder and the repository’s JSON/TSV metadata. Other runs in that folder may also be downloaded. MNE then opens the cached recording lazily.

This dataset also includes 18 anatomical NIfTI images in the nifti column:

from datasets import load_dataset


images = load_dataset("EEGDash/ds002718", datatypes="anat", split="train")
print(images[0]["nifti"].shape)

Source and attribution

• Source: https://nemar.org/dataset/on002718
• Snapshot manifest: https://data.nemar.org/on002718/v1.0.0/manifest.json
• Original authors, license and citations: source README and source metadata.
• Machine-readable checksums: nemar_manifest.json.
• Mirror version and omitted control files: mirror.json.

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