EEGDash/ds005034

---
pretty_name: "The effect of theta tACS on working memory"
license: cc0-1.0
tags:
- eeg
- neuroscience
- eegdash
- brain-computer-interface
- pytorch
size_categories:
- n<1K
task_categories:
- other
---

# The effect of theta tACS on working memory

**Dataset ID:** `ds005034`

_Pavlov2024_effect_theta_tACS_

> **At a glance:** EEG · 25 subjects · 100 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="ds005034", 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/ds005034")
```

## Dataset metadata

| | |
|---|---|
| **Subjects** | 25 |
| **Recordings** | 100 |
| **Tasks (count)** | 2 |
| **Channels** | 129 (×100) |
| **Sampling rate (Hz)** | 1000 (×100) |
| **Total duration (h)** | 34.9 |
| **Size on disk** | 61.4 GB |
| **Recording type** | EEG |
| **Source** | openneuro |
| **License** | CC0 |
| **NEMAR citations** | 1.0 |

## Links

- **DOI:** [10.18112/openneuro.ds005034.v1.0.1](https://doi.org/10.18112/openneuro.ds005034.v1.0.1)
- **OpenNeuro:** [ds005034](https://openneuro.org/datasets/ds005034)
- **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/ds005034). Do not edit this file by hand — update the upstream source and re-run `scripts/push_metadata_stubs.py`._

pretty_name: "θ频段经颅交流电刺激对工作记忆的影响"
license: cc0-1.0
tags:
- 脑电图（EEG）
- 神经科学
- EEGDash
- 脑机接口（brain-computer-interface）
- PyTorch
size_categories:
- 样本量<1000
task_categories:
- 其他

# θ频段经颅交流电刺激对工作记忆的影响

**数据集ID:** `ds005034`

_Pavlov2024_effect_theta_tACS_

> **概览：** 脑电图（EEG） · 25名被试 · 100条记录 · CC0协议

## 加载该数据集

本仓库为**指针型仓库**。原始脑电图数据存储于其官方规范来源（OpenNeuro / NEMAR）；[EEGDash](https://github.com/eegdash/EEGDash) 可按需流式读取该数据并返回适配PyTorch与braindecode框架的数据集。

python
# pip install eegdash
from eegdash import EEGDashDataset

ds = EEGDashDataset(dataset="ds005034", cache_dir="./cache")
print(len(ds), "条记录")

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/ds005034")

## 数据集元数据

| 指标 | 数值 |
|---|---|
| **被试人数** | 25 |
| **记录条数** | 100 |
| **任务（数量）** | 2 |
| **脑电通道数** | 129（×100条记录） |
| **采样率（Hz）** | 1000（×100条记录） |
| **总时长（小时）** | 34.9 |
| **磁盘占用大小** | 61.4 GB |
| **记录类型** | 脑电图（EEG） |
| **数据来源** | OpenNeuro |
| **授权协议** | CC0 |
| **NEMAR引用量** | 1.0 |

## 相关链接

- **DOI:** [10.18112/openneuro.ds005034.v1.0.1](https://doi.org/10.18112/openneuro.ds005034.v1.0.1)
- **OpenNeuro平台:** [ds005034](https://openneuro.org/datasets/ds005034)
- **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/ds005034)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`。_