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nm000277
Mainsah2025-G
nemar
https://openneuro.org/datasets/nm000277
10.13026/0byy-ry86
CC-BY-4.0
{ "library": "eegdash", "class": "EEGDashDataset", "kwargs": { "dataset": "nm000277" } }
https://huggingface.co/spaces/EEGDash/catalog
huggingface-space/scripts/push_metadata_stubs.py

Mainsah2025-G

Dataset ID: nm000277

Mainsah2025_G

Canonical aliases: BigP3BCI_G · BigP3BCI_StudyG

At a glance: EEG · Visual attention · healthy · 20 subjects · 320 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="nm000277", 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 BigP3BCI_G
ds = BigP3BCI_G(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/nm000277")

Dataset metadata

Subjects 20
Recordings 320
Tasks (count) 1
Channels 16 (×320)
Sampling rate (Hz) 256 (×320)
Total duration (h) 7.6
Size on disk 333.2 MB
Recording type EEG
Experimental modality Visual
Paradigm type Attention
Population Healthy
Source nemar
License CC-BY-4.0

Links


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.

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