README.md

metadata

pretty_name: Mainsah2025-Q
license: cc-by-4.0
tags:
  - eeg
  - neuroscience
  - eegdash
  - brain-computer-interface
  - pytorch
  - visual
  - clinical-intervention
  - other
size_categories:
  - n<1K
task_categories:
  - other

Mainsah2025-Q

Dataset ID: nm000321

Mainsah2025_Q

At a glance: EEG · Visual clinical/intervention · other · 36 subjects · 360 recordings · CC-BY-4.0

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="nm000321", cache_dir="./cache")
print(len(ds), "recordings")

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/nm000321")

Dataset metadata

Subjects	36
Recordings	360
Tasks (count)	1
Channels	32 (×360)
Sampling rate (Hz)	256.0000930697907 (×208), 256.00008203487505 (×52), 256 (×43), 256.00010076264726 (×16), 256.0001098418278 (×12), 256.00012071918457 (×12), 256.0001184842897 (×7), 256.00008886963377 (×4), 256.00009694678226 (×3), 256.00010663894057 (×3)
Total duration (h)	13.1
Size on disk	1.1 GB
Recording type	EEG
Experimental modality	Visual
Paradigm type	Clinical/Intervention
Population	Other
Source	nemar
License	CC-BY-4.0

Links

• DOI: 10.13026/0byy-ry86
• NEMAR: nm000321
• 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.