Datasets:

EEGDash
/

nm000229

pretty_name: >-
  Gwilliams et al. 2023 — Introducing MEG-MASC: a high-quality
  magneto-encephalography dataset for evaluating natural speech processing
license: cc0-1.0
tags:
  - eeg
  - neuroscience
  - eegdash
  - brain-computer-interface
  - pytorch
size_categories:
  - 1K<n<10K
task_categories:
  - other

Gwilliams et al. 2023 — Introducing MEG-MASC: a high-quality magneto-encephalography dataset for evaluating natural speech processing

Dataset ID: nm000229

Gwilliams2023

Canonical aliases: MASC_MEG · MEG_MASC

At a glance: EEG · 29 subjects · 1360 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="nm000229", 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 MASC_MEG
ds = MASC_MEG(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/nm000229")

Dataset metadata


Subjects	29
Recordings	1360
Tasks (count)	79
Channels	208 (×196)
Sampling rate (Hz)	1000 (×196)
Size on disk	Unknown
Recording type	EEG
Source	nemar
License	CC0

Gwilliams et al. 2023 — Introducing MEG-MASC: a high-quality magneto-encephalography dataset for evaluating natural speech processing

Load this dataset

Dataset metadata

Links