Metadata stub for nm000253

README.md

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+---
+pretty_name: "Wang et al. 2024 — Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli"
+license: cc-by-4.0
+tags:
+  - ieeg
+  - neuroscience
+  - eegdash
+  - brain-computer-interface
+  - pytorch
+size_categories:
+  - n<1K
+task_categories:
+  - other
+---
+
+# Wang et al. 2024 — Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli
+
+**Dataset ID:** `nm000253`
+
+_Wang2024_et_al_Brain_
+
+**Canonical aliases:** `BrainTreeBank`
+
+> **At a glance:** IEEG · 10 subjects · 26 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="nm000253", 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 BrainTreeBank
+ds = BrainTreeBank(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/nm000253")
+```
+
+
+## Dataset metadata
+
+| | |
+|---|---|
+| **Subjects** | 10 |
+| **Recordings** | 26 |
+| **Tasks (count)** | 1 |
+| **Channels** | 164 (×8), 156 (×3), 166 (×3), 190 (×3), 136 (×3), 248 (×2), 218 (×2), 108 (×1), 158 (×1) |
+| **Sampling rate (Hz)** | 2048 (×26) |
+| **Total duration (h)** | 1.8 |
+| **Size on disk** | 257.3 GB |
+| **Recording type** | IEEG |
+| **Source** | nemar |
+| **License** | CC BY 4.0 |
+
+## Links
+
+- **DOI:** [10.48550/arXiv.2411.08343](https://doi.org/10.48550/arXiv.2411.08343)
+- **NEMAR:** [nm000253](https://nemar.org/dataexplorer/detail?dataset_id=nm000253)
+- **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/nm000253). Do not edit this file by hand — update the upstream source and re-run `scripts/push_metadata_stubs.py`._

eegdash.json

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+{
+  "dataset_id": "nm000253",
+  "title": "Wang et al. 2024 — Brain Treebank: Large-scale intracranial recordings from naturalistic language stimuli",
+  "source": "nemar",
+  "source_url": "https://openneuro.org/datasets/nm000253",
+  "doi": "10.48550/arXiv.2411.08343",
+  "license": "CC BY 4.0",
+  "loader": {
+    "library": "eegdash",
+    "class": "EEGDashDataset",
+    "kwargs": {
+      "dataset": "nm000253"
+    }
+  },
+  "catalog": "https://huggingface.co/spaces/EEGDash/catalog",
+  "generated_by": "huggingface-space/scripts/push_metadata_stubs.py"
+}