metadata
pretty_name: TX15
license: cc0-1.0
tags:
- eeg
- neuroscience
- eegdash
- brain-computer-interface
- pytorch
size_categories:
- n<1K
task_categories:
- other
TX15
Dataset ID: ds004842
Larkin2023_TX15
Canonical aliases: TX15
At a glance: EEG · 14 subjects · 102 recordings · CC0
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="ds004842", 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 TX15
ds = TX15(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/ds004842")
Dataset metadata
| Subjects | 14 |
| Recordings | 102 |
| Tasks (count) | 1 |
| Channels | 70 (×94), 72 (×8) |
| Sampling rate (Hz) | 256 (×102) |
| Total duration (h) | 20.2 |
| Size on disk | 5.2 GB |
| Recording type | EEG |
| Source | openneuro |
| License | CC0 |
| NEMAR citations | 0.0 |
Links
- DOI: 10.18112/openneuro.ds004842.v1.0.0
- OpenNeuro: ds004842
- 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.