EEGDash/ds007615

--- pretty_name: "LDAEP and resting-state EEG in healthy women" license: cc0-1.0 tags: - eeg - neuroscience - eegdash - brain-computer-interface - pytorch - auditory - perception size_categories: - n<1K task_categories: - other --- # LDAEP and resting-state EEG in healthy women **Dataset ID:** `ds007615` _Normannseth2026_ **Canonical aliases:** `Normannseth2026` > **At a glance:** EEG · Auditory perception · healthy · 69 subjects · 192 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="ds007615", 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 Normannseth2026 ds = Normannseth2026(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/ds007615") ``` ## Dataset metadata | | | |---|---| | **Subjects** | 69 | | **Recordings** | 192 | | **Tasks (count)** | 2 | | **Channels** | 68 (×192) | | **Sampling rate (Hz)** | 2048 (×192) | | **Total duration (h)** | 18.5 | | **Size on disk** | 34.6 GB | | **Recording type** | EEG | | **Experimental modality** | Auditory | | **Paradigm type** | Perception | | **Population** | Healthy | | **Source** | openneuro | | **License** | CC0 | ## Links - **DOI:** [10.18112/openneuro.ds007615.v1.0.0](https://doi.org/10.18112/openneuro.ds007615.v1.0.0) - **OpenNeuro:** [ds007615](https://openneuro.org/datasets/ds007615) - **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/ds007615). Do not edit this file by hand — update the upstream source and re-run `scripts/push_metadata_stubs.py`._--- pretty_name: "健康女性的长潜伏期听觉诱发电位（LDAEP）与静息态脑电图" license: cc0-1.0 tags: - 脑电图（EEG） - 神经科学 - EEGDash - 脑机接口（brain-computer-interface） - PyTorch - 听觉 - 感知 size_categories: - n<1K task_categories: - 其他 --- # 健康女性的长潜伏期听觉诱发电位（LDAEP）与静息态脑电图 **数据集ID：** `ds007615` _Normannseth2026_ **标准别名：** `Normannseth2026` > **概览：** 脑电图（EEG）、听觉感知、健康人群、69名受试者、192条记录、CC0协议 ## 加载此数据集 本仓库为**索引指针**。原始脑电图（EEG）数据存储于其官方源（OpenNeuro / NEMAR）；[EEGDash](https://github.com/eegdash/EEGDash) 支持按需流式读取并返回PyTorch / braindecode格式的数据集。 python # 安装eegdash from eegdash import EEGDashDataset ds = EEGDashDataset(dataset="ds007615", cache_dir="./cache") print(len(ds), "条记录") 您也可以通过标准别名加载此数据集——这些类已在`eegdash.dataset`中注册： python from eegdash.dataset import Normannseth2026 ds = Normannseth2026(cache_dir="./cache") 若此数据集已按照braindecode的Zarr格式镜像至Hugging Face Hub，您也可以直接拉取： python from braindecode.datasets import BaseConcatDataset ds = BaseConcatDataset.pull_from_hub("EEGDash/ds007615") ## 数据集元数据 | | | |---|---| | **受试者人数** | 69 | | **记录条数** | 192 | | **任务（数量）** | 2 | | **通道数** | 68（×192） | | **采样率（Hz）** | 2048（×192） | | **总时长（小时）** | 18.5 | | **磁盘占用大小** | 34.6 GB | | **记录类型** | 脑电图（EEG） | | **实验模态** | 听觉 | | **范式类型** | 感知 | | **研究人群** | 健康人群 | | **数据来源** | OpenNeuro | | **许可证** | CC0 | ## 相关链接 - **DOI：** [10.18112/openneuro.ds007615.v1.0.0](https://doi.org/10.18112/openneuro.ds007615.v1.0.0) - **OpenNeuro：** [ds007615](https://openneuro.org/datasets/ds007615) - **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/ds007615) 自动生成。请勿手动编辑此文件——请更新上游源并重新运行 `scripts/push_metadata_stubs.py` 脚本。