Datasets:

EEGDash
/

ds002908

pretty_name: Human MEG recordings during sequential conflict task
license: cc0-1.0
tags:
  - meg
  - neuroscience
  - eegdash
  - brain-computer-interface
  - pytorch
  - unknown
  - attention
size_categories:
  - n<1K
task_categories:
  - other

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

Human MEG recordings during sequential conflict task

Load this dataset

Dataset metadata

Links