EEGDash/ds005420

---
pretty_name: "Resting state EEG with closed eyes and open eyes in females from 60 to 80 years old"
license: cc0-1.0
tags:
- eeg
- neuroscience
- eegdash
- brain-computer-interface
- pytorch
- resting-state
size_categories:
- n<1K
task_categories:
- other
---

# Resting state EEG with closed eyes and open eyes in females from 60 to 80 years old

**Dataset ID:** `ds005420`

_Gama2024_

**Canonical aliases:** `Gama2019`

> **At a glance:** EEG · Resting State resting-state · healthy · 37 subjects · 72 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="ds005420", cache_dir="./cache")
print(len(ds), "recordings")
```

You can also load it by canonical alias — these are registered classes in `eegdash.dataset`:

```python
from eegdash.dataset import Gama2019
ds = Gama2019(cache_dir="./cache")
```

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/ds005420")
```

## Dataset metadata

| | |
|---|---|
| **Subjects** | 37 |
| **Recordings** | 72 |
| **Tasks (count)** | 2 |
| **Channels** | 20 (×72) |
| **Sampling rate (Hz)** | 500 (×72) |
| **Total duration (h)** | 5.4 |
| **Size on disk** | 372.1 MB |
| **Recording type** | EEG |
| **Experimental modality** | Resting State |
| **Paradigm type** | Resting-state |
| **Population** | Healthy |
| **Source** | openneuro |
| **License** | CC0 |
| **NEMAR citations** | 0.0 |

## Links

- **DOI:** [10.18112/openneuro.ds005420.v1.0.0](https://doi.org/10.18112/openneuro.ds005420.v1.0.0)
- **OpenNeuro:** [ds005420](https://openneuro.org/datasets/ds005420)
- **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/ds005420). Do not edit this file by hand — update the upstream source and re-run `scripts/push_metadata_stubs.py`._

数据集展示名："60至80岁女性闭眼与睁眼静息态脑电图（Resting State EEG）"
授权协议：CC0 1.0
标签：
- 脑电图（EEG）
- 神经科学
- EEGDash
- 脑机接口（Brain-Computer Interface）
- PyTorch
- 静息态（Resting State）
数据规模分类：
- 样本量少于1000（n<1K）
任务类别：
- 其他

# 60至80岁女性闭眼与睁眼静息态脑电图数据集

**数据集ID：** `ds005420`

_Gama2024_

**规范别名：** `Gama2019`

> **概览：** 脑电图（EEG）· 静息态（Resting State）· 健康人群 · 37名受试者 · 72次记录 · CC0

## 加载此数据集

本仓库为**指针型仓库**，原始脑电图（EEG）数据存储于其规范来源（OpenNeuro / NEMAR）；[EEGDash](https://github.com/eegdash/EEGDash) 可按需流式读取并返回PyTorch / braindecode格式的数据集。

python
# 安装eegdash
from eegdash import EEGDashDataset

ds = EEGDashDataset(dataset="ds005420", cache_dir="./cache")
print(len(ds), "次记录")

你也可以通过规范别名加载——这些是`eegdash.dataset`中注册的类：

python
from eegdash.dataset import Gama2019
ds = Gama2019(cache_dir="./cache")

若该数据集已按照braindecode的Zarr格式镜像至Hugging Face Hub，你也可以直接拉取：

python
from braindecode.datasets import BaseConcatDataset
ds = BaseConcatDataset.pull_from_hub("EEGDash/ds005420")

## 数据集元数据

| 元数据项 | 数值 |
|---|---|
| **受试者数量** | 37 |
| **记录次数** | 72 |
| **任务（数量）** | 2 |
| **通道数** | 20（共72次记录） |
| **采样率（Hz）** | 500（共72次记录） |
| **总时长（h）** | 5.4 |
| **磁盘占用量** | 372.1 MB |
| **记录类型** | 脑电图（EEG） |
| **实验模态** | 静息态（Resting State） |
| **范式类型** | 静息态（Resting-state） |
| **研究人群** | 健康人群 |
| **数据来源** | OpenNeuro |
| **授权协议** | CC0 |
| **NEMAR引用量** | 0.0 |

## 相关链接

- **数字对象标识符（DOI）：** [10.18112/openneuro.ds005420.v1.0.0](https://doi.org/10.18112/openneuro.ds005420.v1.0.0)
- **OpenNeuro平台：** [ds005420](https://openneuro.org/datasets/ds005420)
- **浏览700+数据集：** [EEGDash数据集目录](https://huggingface.co/spaces/EEGDash/catalog)
- **文档：** <https://eegdash.org>
- **代码仓库：** <https://github.com/eegdash/EEGDash>

---

_本文件由[dataset_summary.csv](https://github.com/eegdash/EEGDash/blob/main/eegdash/dataset/dataset_summary.csv)及[EEGDash API](https://data.eegdash.org/api/eegdash/datasets/summary/ds005420)自动生成。请勿手动编辑此文件——请更新上游数据源并重新运行`scripts/push_metadata_stubs.py`。_