metadata
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 streams it on demand and returns a PyTorch / braindecode dataset.
# 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:
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:
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
- NEMAR: nm000253
- Browse 700+ datasets: EEGDash catalog
- Docs: https://eegdash.org
- Code: https://github.com/eegdash/EEGDash
Auto-generated from dataset_summary.csv and the EEGDash API. Do not edit this file by hand — update the upstream source and re-run scripts/push_metadata_stubs.py.