EEGDash/ds005087

---
pretty_name: "rapid-hemifield-object-eeg"
license: cc0-1.0
tags:
- eeg
- neuroscience
- eegdash
- brain-computer-interface
- pytorch
- visual
- perception
size_categories:
- n<1K
task_categories:
- other
---

# rapid-hemifield-object-eeg

**Dataset ID:** `ds005087`

_Robinson2024_rapid_

> **At a glance:** EEG · Visual perception · healthy · 20 subjects · 60 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="ds005087", 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/ds005087")
```

## Dataset metadata

| | |
|---|---|
| **Subjects** | 20 |
| **Recordings** | 60 |
| **Tasks (count)** | 3 |
| **Channels** | 63 (×60) |
| **Sampling rate (Hz)** | 1000 (×60) |
| **Total duration (h)** | 14.4 |
| **Size on disk** | 12.2 GB |
| **Recording type** | EEG |
| **Experimental modality** | Visual |
| **Paradigm type** | Perception |
| **Population** | Healthy |
| **Source** | openneuro |
| **License** | CC0 |
| **NEMAR citations** | 1.0 |

## Links

- **DOI:** [10.18112/openneuro.ds005087.v1.0.1](https://doi.org/10.18112/openneuro.ds005087.v1.0.1)
- **OpenNeuro:** [ds005087](https://openneuro.org/datasets/ds005087)
- **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/ds005087). Do not edit this file by hand — update the upstream source and re-run `scripts/push_metadata_stubs.py`._

---
pretty_name: "快速半视野物体脑电图（rapid-hemifield-object-eeg）"
license: cc0-1.0
tags:
- 脑电图（EEG）
- 神经科学
- EEGDash
- 脑机接口（brain-computer-interface）
- PyTorch
- 视觉
- 感知
size_categories:
- n<1K
task_categories:
- 其他
---

# 快速半视野物体脑电图数据集

**数据集ID：`ds005087`**

_Robinson2024_rapid_

> **快速概览：** 脑电图 · 视觉感知 · 健康受试人群 · 20名受试者 · 60次记录 · CC0许可

## 加载此数据集

本仓库为**指针仓库**，原始脑电图数据存储于其标准来源（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
# pip install eegdash
from eegdash import EEGDashDataset

ds = EEGDashDataset(dataset="ds005087", cache_dir="./cache")
print(len(ds), "次记录")

若该数据集已按照braindecode的Zarr布局镜像至Hugging Face Hub，也可直接拉取：

python
from braindecode.datasets import BaseConcatDataset
ds = BaseConcatDataset.pull_from_hub("EEGDash/ds005087")

## 数据集元数据

| | |
|---|---|
| **受试者数量** | 20 |
| **记录次数** | 60 |
| **任务（数量）** | 3 |
| **通道数** | 63（共60次记录） |
| **采样率（Hz）** | 1000（共60次记录） |
| **总时长（小时）** | 14.4 |
| **磁盘占用大小** | 12.2 GB |
| **记录类型** | 脑电图（EEG） |
| **实验模态** | 视觉 |
| **范式类型** | 感知 |
| **受试人群** | 健康人群 |
| **数据来源** | OpenNeuro |
| **许可证** | CC0 |
| **NEMAR引用量** | 1.0 |

## 链接

- **DOI:** [10.18112/openneuro.ds005087.v1.0.1](https://doi.org/10.18112/openneuro.ds005087.v1.0.1)
- **OpenNeuro:** [ds005087](https://openneuro.org/datasets/ds005087)
- **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)与[EEGDash API](https://data.eegdash.org/api/eegdash/datasets/summary/ds005087)自动生成。请勿手动编辑此文件，请更新上游数据源后重新运行`scripts/push_metadata_stubs.py`。