EEGDash/nm000121

--- pretty_name: "Oikonomou2016 – SSVEP MAMEM 3 dataset" license: other tags: - eeg - neuroscience - eegdash - brain-computer-interface - pytorch - visual - perception size_categories: - n<1K task_categories: - other --- # Oikonomou2016 – SSVEP MAMEM 3 dataset **Dataset ID:** `nm000121` _Oikonomou2016_MAMEM3_ **Canonical aliases:** `MAMEM3` · `SSVEP_MAMEM3` > **At a glance:** EEG · Visual perception · healthy · 11 subjects · 110 recordings · ODC-By-1.0 ## 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="nm000121", 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 MAMEM3 ds = MAMEM3(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/nm000121") ``` ## Dataset metadata | | | |---|---| | **Subjects** | 11 | | **Recordings** | 110 | | **Tasks (count)** | 1 | | **Channels** | 14 (×110) | | **Sampling rate (Hz)** | 128 (×110) | | **Total duration (h)** | 4.6 | | **Size on disk** | 120.2 MB | | **Recording type** | EEG | | **Experimental modality** | Visual | | **Paradigm type** | Perception | | **Population** | Healthy | | **Source** | nemar | | **License** | ODC-By-1.0 | ## Links - **NEMAR:** [nm000121](https://nemar.org/dataexplorer/detail?dataset_id=nm000121) - **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/nm000121). Do not edit this file by hand — update the upstream source and re-run `scripts/push_metadata_stubs.py`._--- pretty_name: "Oikonomou2016 – 稳态视觉诱发电位（Steady-State Visual Evoked Potentials, SSVEP）MAMEM 3 数据集" license: "其他" tags: - 脑电图（Electroencephalogram, EEG） - 神经科学 - eegdash - 脑机接口（Brain-Computer Interface, BCI） - PyTorch - 视觉 - 感知 size_categories: - 样本量少于1000 task_categories: - 其他 --- # Oikonomou2016 – 稳态视觉诱发电位（Steady-State Visual Evoked Potentials, SSVEP）MAMEM 3 数据集 **数据集ID：** `nm000121` _Oikonomou2016_MAMEM3_ **标准别名：** `MAMEM3` · `SSVEP_MAMEM3` > **概览：** 脑电图（Electroencephalogram, EEG）、视觉感知、健康受试人群、11名受试者、110条记录、ODC-By-1.0 许可协议 ## 数据集加载方式 本仓库仅为**索引指针**。原始脑电图数据存储于其官方源（OpenNeuro / NEMAR）；[EEGDash](https://github.com/eegdash/EEGDash) 可按需流式加载该数据集，并返回 PyTorch / braindecode 格式的数据集对象。 python # 安装依赖：eegdash from eegdash import EEGDashDataset ds = EEGDashDataset(dataset="nm000121", cache_dir="./cache") print(len(ds), "条记录") 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 MAMEM3 ds = MAMEM3(cache_dir="./cache") 若该数据集已按照 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/nm000121") ## 数据集元数据 | 指标 | 数值 | |---|---| | **受试者数量** | 11 | | **记录条数** | 110 | | **任务数量** | 1 | | **通道数** | 14（每条记录） | | **采样率（Hz）** | 128（每条记录） | | **总时长（小时）** | 4.6 | | **磁盘占用大小** | 120.2 MB | | **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 | | **许可协议** | ODC-By-1.0 | ## 相关链接 - **NEMAR平台：** [nm000121](https://nemar.org/dataexplorer/detail?dataset_id=nm000121) - **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/nm000121) 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。_