EEGDash/nm000134

---
pretty_name: "Alljoined-1.6M"
license: other
tags:
- eeg
- neuroscience
- eegdash
- brain-computer-interface
- pytorch
size_categories:
- 1K<n<10K
task_categories:
- other
---

# Alljoined-1.6M

**Dataset ID:** `nm000134`

_Xu2025_

**Canonical aliases:** `Alljoined16M` · `Alljoined_16M` · `Alljoined1p6M`

> **At a glance:** EEG · 20 subjects · 1525 recordings · CC-BY-NC-ND-4.0

## 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="nm000134", 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 Alljoined16M
ds = Alljoined16M(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/nm000134")
```

## Dataset metadata

| | |
|---|---|
| **Subjects** | 20 |
| **Recordings** | 1525 |
| **Tasks (count)** | 1 |
| **Channels** | 32 (×1525) |
| **Sampling rate (Hz)** | 256 (×1525) |
| **Total duration (h)** | 129.5 |
| **Size on disk** | 8.2 GB |
| **Recording type** | EEG |
| **Source** | nemar |
| **License** | CC-BY-NC-ND-4.0 |

## Links

- **DOI:** [10.82901/nemar.nm000134](https://doi.org/10.82901/nemar.nm000134)
- **NEMAR:** [nm000134](https://nemar.org/dataexplorer/detail?dataset_id=nm000134)
- **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/nm000134). Do not edit this file by hand — update the upstream source and re-run `scripts/push_metadata_stubs.py`._

pretty_name: "Alljoined-1.6M"
license: other
tags:
- 脑电图（EEG）
- 神经科学（Neuroscience）
- EEGDash
- 脑机接口（brain-computer interface）
- PyTorch（PyTorch）
size_categories:
- 1K<n<10K
task_categories:
- other

# Alljoined-1.6M

**数据集ID：** `nm000134`

_引用：Xu2025_

**规范别名：** `Alljoined16M` · `Alljoined_16M` · `Alljoined1p6M`

> **概览：** 脑电图（EEG） · 20名受试者 · 1525条记录 · CC-BY-NC-ND-4.0 许可协议

## 加载此数据集
本仓库仅为**指针文件**，原始脑电图（EEG）数据存储于其规范源地址（OpenNeuro / NEMAR）；[EEGDash](https://github.com/eegdash/EEGDash) 支持按需流式读取该数据集，并返回PyTorch（PyTorch）/ braindecode 格式的数据集对象。

python
# pip install eegdash
from eegdash import EEGDashDataset

ds = EEGDashDataset(dataset="nm000134", cache_dir="./cache")
print(len(ds), "条记录")

你也可以通过规范别名加载该数据集——这些别名已在`eegdash.dataset`中注册为类：
python
from eegdash.dataset import Alljoined16M
ds = Alljoined16M(cache_dir="./cache")

若该数据集已按照braindecode的Zarr布局镜像至Hugging Face Hub，你也可以直接拉取：
python
from braindecode.datasets import BaseConcatDataset
ds = BaseConcatDataset.pull_from_hub("EEGDash/nm000134")

## 数据集元数据

| 指标 | 详情 |
|---|---|
| **受试者数量** | 20 |
| **记录条数** | 1525 |
| **任务数（计数）** | 1 |
| **通道数** | 32（每条记录） |
| **采样率（赫兹）** | 256（每条记录） |
| **总时长（小时）** | 129.5 |
| **磁盘占用大小** | 8.2 GB |
| **记录类型** | 脑电图（EEG） |
| **数据来源** | nemar |
| **许可协议** | CC-BY-NC-ND-4.0 |

## 相关链接
- **数字对象标识符（DOI）：** [10.82901/nemar.nm000134](https://doi.org/10.82901/nemar.nm000134)
- **NEMAR平台：** [nm000134](https://nemar.org/dataexplorer/detail?dataset_id=nm000134)
- **浏览700+数据集：** [EEGDash数据集目录](https://huggingface.co/spaces/EEGDash/catalog)
- **官方文档：** <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/nm000134) 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`。_