EEGDash/ds005964

---
pretty_name: "FRESH Audio Dataset"
license: cc0-1.0
tags:
- eeg
- neuroscience
- eegdash
- brain-computer-interface
- pytorch
- auditory
- perception
size_categories:
- n<1K
task_categories:
- other
---

# FRESH Audio Dataset

**Dataset ID:** `ds005964`

_Luke2025_

**Canonical aliases:** `Luke2019`

> **At a glance:** FNIRS · Auditory perception · unknown · 17 subjects · 17 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="ds005964", 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 Luke2019
ds = Luke2019(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/ds005964")
```

## Dataset metadata

| | |
|---|---|
| **Subjects** | 17 |
| **Recordings** | 17 |
| **Tasks (count)** | 1 |
| **Channels** | 66 (×17) |
| **Sampling rate (Hz)** | 5.208333333333333 (×17) |
| **Total duration (h)** | 6.1 |
| **Size on disk** | 62.4 MB |
| **Recording type** | FNIRS |
| **Experimental modality** | Auditory |
| **Paradigm type** | Perception |
| **Population** | Unknown |
| **Source** | openneuro |
| **License** | CC0 |

## Links

- **DOI:** [10.18112/openneuro.ds005964.v1.0.0](https://doi.org/10.18112/openneuro.ds005964.v1.0.0)
- **OpenNeuro:** [ds005964](https://openneuro.org/datasets/ds005964)
- **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/ds005964). Do not edit this file by hand — update the upstream source and re-run `scripts/push_metadata_stubs.py`._

---
pretty_name: "FRESH音频数据集（FRESH Audio Dataset）"
license: cc0-1.0
tags:
- 脑电图（EEG）
- 神经科学
- EEGDash
- 脑机接口（brain-computer-interface）
- PyTorch
- 听觉
- 感知
size_categories:
- 样本数少于1000
task_categories:
- 其他
---

# FRESH音频数据集（FRESH Audio Dataset）

**数据集ID：** `ds005964`

_Luke2025_

**标准别名：** `Luke2019`

> **概览：** 功能性近红外光谱（FNIRS） · 听觉感知 · 未知人群 · 17名受试者 · 17条记录 · CC0

## 数据集加载

本仓库为**指针型仓库**。原始脑电图（EEG）数据存储于其标准来源（OpenNeuro / NEMAR）；[EEGDash](https://github.com/eegdash/EEGDash) 可按需流式加载该数据，并返回PyTorch / braindecode格式的数据集。

python
# pip install eegdash
from eegdash import EEGDashDataset

ds = EEGDashDataset(dataset="ds005964", cache_dir="./cache")
print(len(ds), "recordings")

你也可以通过标准别名加载该数据集——这些类已在`eegdash.dataset`中注册：

python
from eegdash.dataset import Luke2019
ds = Luke2019(cache_dir="./cache")

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

python
from braindecode.datasets import BaseConcatDataset
ds = BaseConcatDataset.pull_from_hub("EEGDash/ds005964")

## 数据集元数据

| 元数据项 | 详情 |
|---|---|
| **受试者数量** | 17 |
| **记录条数** | 17 |
| **任务数** | 1 |
| **通道数** | 66（×17） |
| **采样率（Hz）** | 5.208333333333333（×17） |
| **总时长（h）** | 6.1 |
| **磁盘占用大小** | 62.4 MB |
| **记录类型** | 功能性近红外光谱（FNIRS） |
| **实验模态** | 听觉 |
| **实验范式类型** | 感知 |
| **受试人群** | 未知 |
| **数据来源** | OpenNeuro |
| **授权协议** | CC0 |

## 相关链接

- **DOI：** [10.18112/openneuro.ds005964.v1.0.0](https://doi.org/10.18112/openneuro.ds005964.v1.0.0)
- **OpenNeuro：** [ds005964](https://openneuro.org/datasets/ds005964)
- **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/ds005964)自动生成。请勿手动编辑本文件——请更新上游数据源并重新运行`scripts/push_metadata_stubs.py`脚本。_