EEGDash/nm000129

--- pretty_name: "Liu2020 – BETA SSVEP benchmark dataset" license: other tags: - eeg - neuroscience - eegdash - brain-computer-interface - pytorch - visual - perception size_categories: - n<1K task_categories: - other --- # Liu2020 – BETA SSVEP benchmark dataset **Dataset ID:** `nm000129` _Liu2020_ **Canonical aliases:** `BetaSSVEP` · `BETA_SSVEP` · `BETA` > **At a glance:** EEG · Visual perception · healthy · 70 subjects · 70 recordings · Non-commercial research use ## 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="nm000129", 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 BetaSSVEP ds = BetaSSVEP(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/nm000129") ``` ## Dataset metadata | | | |---|---| | **Subjects** | 70 | | **Recordings** | 70 | | **Tasks (count)** | 1 | | **Channels** | 64 (×70) | | **Sampling rate (Hz)** | 250 (×70) | | **Total duration (h)** | 13.0 | | **Size on disk** | 2.8 GB | | **Recording type** | EEG | | **Experimental modality** | Visual | | **Paradigm type** | Perception | | **Population** | Healthy | | **Source** | nemar | | **License** | Non-commercial research use | ## Links - **NEMAR:** [nm000129](https://nemar.org/dataexplorer/detail?dataset_id=nm000129) - **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/nm000129). Do not edit this file by hand — update the upstream source and re-run `scripts/push_metadata_stubs.py`._--- pretty_name: "刘等2020年——BETA稳态视觉诱发电位（Steady-State Visual Evoked Potential, SSVEP）基准数据集" license: 其他 tags: - 脑电图（Electroencephalogram, EEG） - 神经科学 - EEGDash - 脑机接口（brain-computer interface） - PyTorch - 视觉 - 感知 size_categories: - 样本数少于1000 task_categories: - 其他 --- # 刘等2020年——BETA稳态视觉诱发电位（Steady-State Visual Evoked Potential, SSVEP）基准数据集 **数据集ID：** `nm000129` _刘等2020年_ **标准别名：** `BetaSSVEP` · `BETA_SSVEP` · `BETA` > **概览：** 脑电图（EEG）· 视觉感知 · 健康人群 · 70名受试者 · 70条记录 · 仅可用于非商业性研究用途 ## 加载此数据集 本仓库仅为**索引指针**。原始脑电图数据存储于其官方源地址（OpenNeuro / NEMAR）；[EEGDash](https://github.com/eegdash/EEGDash) 可按需流式加载该数据集，并返回适用于PyTorch / braindecode的数据集格式。 python # pip install eegdash from eegdash import EEGDashDataset ds = EEGDashDataset(dataset="nm000129", cache_dir="./cache") print(len(ds), "recordings") 你也可以通过标准别名加载该数据集——这些别名是`eegdash.dataset`中已注册的类： python from eegdash.dataset import BetaSSVEP ds = BetaSSVEP(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/nm000129") ## 数据集元数据 | 指标 | 数值 | |---|---| | **受试者数量** | 70 | | **记录条数** | 70 | | **任务（数量）** | 1 | | **通道数** | 64（×70） | | **采样率（赫兹）** | 250（×70） | | **总时长（小时）** | 13.0 | | **磁盘占用** | 2.8 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 | | **数据源** | nemar | | **授权协议** | 非商业研究用途 | ## 相关链接 - **NEMAR平台：** [nm000129](https://nemar.org/dataexplorer/detail?dataset_id=nm000129) - **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> - **源代码：** <https://github.com/eegdash/EEGDash> --- _本内容由[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/nm000129)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`脚本以更新内容。_