EEGDash/ds006817

--- pretty_name: "Visual Attribute-Specific Contextual Trajectory Paradigm 2.0" license: cc0-1.0 tags: - eeg - neuroscience - eegdash - brain-computer-interface - pytorch - unknown size_categories: - n<1K task_categories: - other --- # Visual Attribute-Specific Contextual Trajectory Paradigm 2.0 **Dataset ID:** `ds006817` _Lowe2025_ **Canonical aliases:** `VisualContextTrajectory_v2` · `Lowe2025` > **At a glance:** EEG · Unknown unknown · unknown · 34 subjects · 34 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="ds006817", 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 VisualContextTrajectory_v2 ds = VisualContextTrajectory_v2(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/ds006817") ``` ## Dataset metadata | | | |---|---| | **Subjects** | 34 | | **Recordings** | 34 | | **Tasks (count)** | 1 | | **Channels** | 65 (×34) | | **Sampling rate (Hz)** | 1024 (×34) | | **Total duration (h)** | 21.7 | | **Size on disk** | 9.7 GB | | **Recording type** | EEG | | **Experimental modality** | Unknown | | **Paradigm type** | Unknown | | **Population** | Unknown | | **Source** | openneuro | | **License** | CC0 | ## Links - **DOI:** [10.18112/openneuro.ds006817.v1.0.0](https://doi.org/10.18112/openneuro.ds006817.v1.0.0) - **OpenNeuro:** [ds006817](https://openneuro.org/datasets/ds006817) - **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/ds006817). Do not edit this file by hand — update the upstream source and re-run `scripts/push_metadata_stubs.py`._# 数据集基本配置 - 数据集名称：视觉属性特定上下文轨迹范式2.0（Visual Attribute-Specific Contextual Trajectory Paradigm 2.0） - 授权协议：CC0 1.0 - 标签：脑电图（EEG）、神经科学、EEGDash、脑机接口（brain-computer-interface）、PyTorch、未知 - 数据规模分类：样本量＜1000 - 任务类别：其他 --- ## 视觉属性特定上下文轨迹范式2.0 **数据集ID：`ds006817`** _Lowe 2025_ **标准别名：`VisualContextTrajectory_v2` · `Lowe2025`** > **概览：** 脑电图（EEG）、未知未知、未知、34名受试者、34次记录、CC0协议 ## 加载该数据集 本仓库仅为**索引指针**。原始脑电图（EEG）数据存储于其标准数据源（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 # 安装EEGDash库 from eegdash import EEGDashDataset ds = EEGDashDataset(dataset="ds006817", cache_dir="./cache") print(len(ds), "次记录") 你也可以通过标准别名加载该数据集——这些别名对应`eegdash.dataset`中已注册的类： python from eegdash.dataset import VisualContextTrajectory_v2 ds = VisualContextTrajectory_v2(cache_dir="./cache") 若该数据集已以braindecode的Zarr格式镜像至Hugging Face Hub，你也可以直接拉取： python from braindecode.datasets import BaseConcatDataset ds = BaseConcatDataset.pull_from_hub("EEGDash/ds006817") ## 数据集元数据 | 字段 | 数值 | |---|---| | **受试者数量** | 34 | | **记录次数** | 34 | | **任务（数量）** | 1 | | **通道数** | 65（×34） | | **采样率（赫兹）** | 1024（×34） | | **总时长（小时）** | 21.7 | | **磁盘占用大小** | 9.7 GB | | **记录类型** | 脑电图（EEG） | | **实验模态** | 未知 | | **范式类型** | 未知 | | **研究人群** | 未知 | | **数据源** | OpenNeuro | | **授权协议** | CC0 | ## 相关链接 - **DOI：** [10.18112/openneuro.ds006817.v1.0.0](https://doi.org/10.18112/openneuro.ds006817.v1.0.0) - **OpenNeuro平台：** [ds006817](https://openneuro.org/datasets/ds006817) - **浏览700+数据集：** [EEGDash数据集目录](https://huggingface.co/spaces/EEGDash/catalog) - **文档地址：** <https://eegdash.org> - **代码仓库：** <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/ds006817)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。_