--- pretty_name: "Door lock control experiment (15 subjects, 4 classes, 31 EEG ch)" license: cc-by-4.0 tags: - eeg - neuroscience - eegdash - brain-computer-interface - pytorch - visual - attention size_categories: - n<1K task_categories: - other --- # Door lock control experiment (15 subjects, 4 classes, 31 EEG ch) **Dataset ID:** `nm000208` _Lee2024_Door_lock_control_ > **At a glance:** EEG · Visual attention · healthy · 14 subjects · 434 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="nm000208", 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/nm000208") ``` ## Dataset metadata | | | |---|---| | **Subjects** | 14 | | **Recordings** | 434 | | **Tasks (count)** | 1 | | **Channels** | 31 (×434) | | **Sampling rate (Hz)** | 500 (×434) | | **Total duration (h)** | 3.7 | | **Size on disk** | 609.6 MB | | **Recording type** | EEG | | **Experimental modality** | Visual | | **Paradigm type** | Attention | | **Population** | Healthy | | **Source** | nemar | | **License** | CC-BY-4.0 | ## Links - **NEMAR:** [nm000208](https://nemar.org/dataexplorer/detail?dataset_id=nm000208) - **Browse 700+ datasets:** [EEGDash catalog](https://huggingface.co/spaces/EEGDash/catalog) - **Docs:** - **Code:** --- _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/nm000208). Do not edit this file by hand — update the upstream source and re-run `scripts/push_metadata_stubs.py`._