Datasets:

EEGDash
/

ds000248

pretty_name: MNE-Sample-Data
license: cc0-1.0
tags:
  - meg
  - neuroscience
  - eegdash
  - brain-computer-interface
  - pytorch
  - multisensory
  - attention
size_categories:
  - n<1K
task_categories:
  - other

MNE-Sample-Data

Dataset ID: ds000248

Gramfort2018

Canonical aliases: MNE_Sample_Data

At a glance: MEG · Multisensory attention · healthy · 2 subjects · 3 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="ds000248", 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 MNE_Sample_Data
ds = MNE_Sample_Data(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/ds000248")

Dataset metadata


Subjects	2
Recordings	3
Tasks (count)	2
Channels	376 (×1), 315 (×1)
Sampling rate (Hz)	600.614990234375 (×2)
Total duration (h)	0.1
Size on disk	177.6 MB
Recording type	MEG
Experimental modality	Multisensory
Paradigm type	Attention
Population	Healthy
Source	openneuro
License	CC0
NEMAR citations	3.0

MNE-Sample-Data

Load this dataset

Dataset metadata

Links