EEGDash/nm000125

--- pretty_name: "Lee2021 – SSVEP paradigm of the Mobile BCI dataset" license: cc-by-4.0 tags: - eeg - neuroscience - eegdash - brain-computer-interface - pytorch - visual - perception size_categories: - n<1K task_categories: - other --- # Lee2021 – SSVEP paradigm of the Mobile BCI dataset **Dataset ID:** `nm000125` _Lee2021_SSVEP_ > **At a glance:** EEG · Visual perception · healthy · 23 subjects · 85 recordings · CC BY 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="nm000125", 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/nm000125") ``` ## Dataset metadata | | | |---|---| | **Subjects** | 23 | | **Recordings** | 85 | | **Tasks (count)** | 1 | | **Channels** | 73 (×84), 46 (×1) | | **Sampling rate (Hz)** | 100 (×85) | | **Total duration (h)** | 13.3 | | **Size on disk** | 1.3 GB | | **Recording type** | EEG | | **Experimental modality** | Visual | | **Paradigm type** | Perception | | **Population** | Healthy | | **Source** | nemar | | **License** | CC BY 4.0 | ## Links - **NEMAR:** [nm000125](https://nemar.org/dataexplorer/detail?dataset_id=nm000125) - **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/nm000125). Do not edit this file by hand — update the upstream source and re-run `scripts/push_metadata_stubs.py`._pretty_name: "Lee2021 – 移动脑机接口（BCI, Brain-Computer Interface）数据集的稳态视觉诱发电位（SSVEP, Steady-State Visual Evoked Potential）范式" license: cc-by-4.0 tags: - 脑电图（EEG, Electroencephalogram） - 神经科学 - eegdash - 脑机接口（BCI, Brain-Computer Interface） - pytorch - 视觉 - 感知 size_categories: - n<1K task_categories: - other # Lee2021 – 移动脑机接口数据集的稳态视觉诱发电位范式 **数据集ID:** `nm000125` _Lee2021_稳态视觉诱发电位数据集_ > **快速概览:** 脑电图（EEG） · 视觉感知 · 健康人群 · 23名受试者 · 85条记录 · CC BY 4.0 ## 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来源（OpenNeuro / NEMAR）；[EEGDash](https://github.com/eegdash/EEGDash) 可按需流式读取该数据集并返回PyTorch / braindecode数据集。 python # 安装eegdash库 from eegdash import EEGDashDataset ds = EEGDashDataset(dataset="nm000125", 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/nm000125") ## 数据集元数据 | | | |---|---| | **受试者数量** | 23 | | **记录条数** | 85 | | **任务（数量）** | 1 | | **通道数** | 73 (×84), 46 (×1) | | **采样率（Hz）** | 100 (×85) | | **总时长（小时）** | 13.3 | | **磁盘占用大小** | 1.3 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** | nemar | | **许可协议** | CC BY 4.0 | ## 相关链接 - **NEMAR平台:** [nm000125](https://nemar.org/dataexplorer/detail?dataset_id=nm000125) - **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) 与 [EEGDash API](https://data.eegdash.org/api/eegdash/datasets/summary/nm000125) 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` 脚本完成更新。_