Datasets:

EEGDash
/

ds006171

pretty_name: >-
  EEG data during three near-threshold visual detection tasks: a no-cue task, a
  noninformative cue task (50% validity), and an informative cue task (100%
  validity)
license: cc0-1.0
tags:
  - eeg
  - neuroscience
  - eegdash
  - brain-computer-interface
  - pytorch
  - visual
  - attention
size_categories:
  - n<1K
task_categories:
  - other

EEG data during three near-threshold visual detection tasks: a no-cue task, a noninformative cue task (50% validity), and an informative cue task (100% validity)

Dataset ID: ds006171

Melcon2025

Canonical aliases: Melcon2024

At a glance: EEG · Visual attention · healthy · 36 subjects · 104 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="ds006171", 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 Melcon2024
ds = Melcon2024(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/ds006171")

Dataset metadata


Subjects	36
Recordings	104
Tasks (count)	3
Channels	144 (×104)
Sampling rate (Hz)	1024 (×104)
Total duration (h)	40.9
Size on disk	67.8 GB
Recording type	EEG
Experimental modality	Visual
Paradigm type	Attention
Population	Healthy
Source	openneuro
License	CC0

EEG data during three near-threshold visual detection tasks: a no-cue task, a noninformative cue task (50% validity), and an informative cue task (100% validity)

Load this dataset

Dataset metadata

Links