metadata
pretty_name: >-
Model-based aversive learning in humans is supported by preferential task
state reactivation
license: cc0-1.0
tags:
- meg
- neuroscience
- eegdash
- brain-computer-interface
- pytorch
- unknown
- learning
size_categories:
- n<1K
task_categories:
- other
Model-based aversive learning in humans is supported by preferential task state reactivation
Dataset ID: ds003682
Wise2021
At a glance: MEG · Unknown learning · healthy · 28 subjects · 336 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="ds003682", 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/ds003682")
Dataset metadata
| Subjects | 28 |
| Recordings | 336 |
| Tasks (count) | 1 |
| Channels | 414 (×336) |
| Sampling rate (Hz) | 1200 (×336) |
| Total duration (h) | 31.8 |
| Size on disk | 211.6 GB |
| Recording type | MEG |
| Experimental modality | Unknown |
| Paradigm type | Learning |
| Population | Healthy |
| Source | openneuro |
| License | CC0 |
| NEMAR citations | 1.0 |
Links
- DOI: 10.18112/openneuro.ds003682.v1.0.0
- OpenNeuro: ds003682
- Browse 700+ datasets: EEGDash catalog
- Docs: https://eegdash.org
- Code: https://github.com/eegdash/EEGDash
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.