EEGDash/ds002908

Human MEG recordings during sequential conflict task

Dataset ID: ds002908

Bogacz2020

Canonical aliases: Bogacz2024

At a glance: MEG · Unknown attention · unknown · 13 subjects · 53 recordings · CC0

Load this dataset

This repo is a pointer. The raw EEG data lives at its canonical source (OpenNeuro / NEMAR); EEGDash streams it on demand and returns a PyTorch / braindecode dataset.

# pip install eegdash
from eegdash import EEGDashDataset

ds = EEGDashDataset(dataset="ds002908", cache_dir="./cache")
print(len(ds), "recordings")

You can also load it by canonical alias — these are registered classes in eegdash.dataset:

from eegdash.dataset import Bogacz2024
ds = Bogacz2024(cache_dir="./cache")

If the dataset has been mirrored to the HF Hub in braindecode's Zarr layout, you can also pull it directly:

from braindecode.datasets import BaseConcatDataset
ds = BaseConcatDataset.pull_from_hub("EEGDash/ds002908")

Dataset metadata

Subjects	13
Recordings	53
Tasks (count)	1
Channels	299 (×53)
Sampling rate (Hz)	2400 (×53)
Total duration (h)	5.1
Size on disk	59.8 GB
Recording type	MEG
Experimental modality	Unknown
Paradigm type	Attention
Population	Unknown
Source	openneuro
License	CC0
NEMAR citations	1.0

Links

• DOI: 10.18112/openneuro.ds002908.v1.0.0
• OpenNeuro: ds002908
• Browse 700+ datasets: EEGDash catalog
• Docs: https://eegdash.org
• Code: https://github.com/eegdash/EEGDash

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Auto-generated from dataset_summary.csv and the EEGDash API. Do not edit this file by hand — update the upstream source and re-run scripts/push_metadata_stubs.py.