---
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](https://github.com/eegdash/EEGDash) streams it
on demand and returns a PyTorch / braindecode dataset.

```python
# 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:

```python
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](https://doi.org/10.18112/openneuro.ds003682.v1.0.0)
- **OpenNeuro:** [ds003682](https://openneuro.org/datasets/ds003682)
- **Browse 700+ datasets:** [EEGDash catalog](https://huggingface.co/spaces/EEGDash/catalog)
- **Docs:** <https://eegdash.org>
- **Code:** <https://github.com/eegdash/EEGDash>

---

_Auto-generated from [dataset_summary.csv](https://github.com/eegdash/EEGDash/blob/main/eegdash/dataset/dataset_summary.csv) and the [EEGDash API](https://data.eegdash.org/api/eegdash/datasets/summary/ds003682). Do not edit this file by hand — update the upstream source and re-run `scripts/push_metadata_stubs.py`._