EEGDash/ds007081

--- pretty_name: "Passive but accessible: Studied information is not actively stored in working memory, yet attended regardless of anticipated load" license: cc0-1.0 tags: - eeg - neuroscience - eegdash - brain-computer-interface - pytorch - visual - memory size_categories: - n<1K task_categories: - other --- # Passive but accessible: Studied information is not actively stored in working memory, yet attended regardless of anticipated load **Dataset ID:** `ds007081` _Ylmaz2025_ > **At a glance:** EEG · Visual memory · healthy · 41 subjects · 41 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="ds007081", 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/ds007081") ``` ## Dataset metadata | | | |---|---| | **Subjects** | 41 | | **Recordings** | 41 | | **Tasks (count)** | 1 | | **Channels** | 32 (×41) | | **Sampling rate (Hz)** | 1000 (×41) | | **Total duration (h)** | 26.3 | | **Size on disk** | 11.3 GB | | **Recording type** | EEG | | **Experimental modality** | Visual | | **Paradigm type** | Memory | | **Population** | Healthy | | **Source** | openneuro | | **License** | CC0 | ## Links - **DOI:** [10.18112/openneuro.ds007081.v1.0.0](https://doi.org/10.18112/openneuro.ds007081.v1.0.0) - **OpenNeuro:** [ds007081](https://openneuro.org/datasets/ds007081) - **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/ds007081). Do not edit this file by hand — update the upstream source and re-run `scripts/push_metadata_stubs.py`._--- pretty_name: "被动却可及：学习所得信息未主动存储于工作记忆，但可随预期负荷被持续注意" license: cc0-1.0 tags: - 脑电图（EEG） - 神经科学 - EEGDash - 脑机接口（brain-computer-interface） - PyTorch - 视觉 - 记忆 size_categories: - 样本量小于1000 task_categories: - 其他 --- # 被动却可及：学习所得信息未主动存储于工作记忆，但可随预期负荷被持续注意 **数据集ID：** `ds007081` _Ylmaz2025_ > **概览：** 脑电图（EEG） · 视觉记忆 · 健康人群 · 41名被试 · 41次记录 · CC0协议 ## 加载该数据集 本仓库为**指针文件**，原始脑电图数据存储于其规范源站（OpenNeuro / NEMAR）；[EEGDash](https://github.com/eegdash/EEGDash) 支持按需流式读取数据，并返回PyTorch / braindecode格式的数据集。 python # pip install eegdash from eegdash import EEGDashDataset ds = EEGDashDataset(dataset="ds007081", cache_dir="./cache") print(len(ds), "次记录") 若该数据集已按照braindecode的Zarr布局镜像至HF Hub，你也可直接拉取该数据集： python from braindecode.datasets import BaseConcatDataset ds = BaseConcatDataset.pull_from_hub("EEGDash/ds007081") ## 数据集元数据 | | | |---|---| | **被试数量** | 41 | | **记录次数** | 41 | | **任务（数量）** | 1 | | **通道数** | 32（共41组） | | **采样率（Hz）** | 1000（共41组） | | **总时长（小时）** | 26.3 | | **磁盘占用大小** | 11.3 GB | | **记录类型** | 脑电图（EEG） | | **实验模态** | 视觉 | | **范式类型** | 记忆 | | **人群特征** | 健康人群 | | **数据来源** | OpenNeuro | | **授权协议** | CC0 | ## 相关链接 - **DOI：** [10.18112/openneuro.ds007081.v1.0.0](https://doi.org/10.18112/openneuro.ds007081.v1.0.0) - **OpenNeuro站点：** [ds007081](https://openneuro.org/datasets/ds007081) - **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/ds007081)自动生成。请勿手动编辑此文件——请更新上游源数据并重新运行`scripts/push_metadata_stubs.py`脚本。_