File size: 4,650 Bytes
6c7cd4e 48a2b2b 6c7cd4e 48a2b2b df83f99 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | ---
license: mit
library_name: braindecode
tags:
- eeg
- event-detection
- dance
- p300
datasets:
- nm000244
---
# DANCE — BI2014a P300 event-detection checkpoint
Pretrained weights for the [`braindecode.models.DANCE`](https://braindecode.org/stable/generated/braindecode.models.DANCE.html)
model, used by the tutorial *"From window labels to events: asynchronous EEG
decoding with DANCE"*.
DANCE detects a *set* of `(start, end, class)` events directly from long,
unaligned EEG windows, without being told where an event starts. This
checkpoint was trained on the Brain Invaders **BI2014a** P300 dataset (flash
detection: class `1` = non-target, class `2` = target, class `0` = background).
## Results
Cross-subject, held-out subject 3 (never seen in training):
| Metric | Value |
|---|---|
| F1-event (IoU > 0.5 + class match) | **0.495** |
| F1-sample (per-token macro) | 0.372 |
On the first held-out window the model predicts 51 events (ground truth: 53),
mean duration ≈ 1.1 s, with confidences 0.75–0.99 — i.e. sharp, correctly
localized P300 flashes.
## How to load
The architecture must be built exactly as in the tutorial (the state dict is
saved with these hyper-parameters):
```python
import torch
from huggingface_hub import hf_hub_download
from braindecode.models import DANCE
model = DANCE(
n_outputs=3,
n_chans=len(chs_info), # 16 for BI2014a
chs_info=chs_info,
n_times=4096, # 32 s @ 128 Hz
sfreq=128.0,
input_window_seconds=32.0,
)
model.load_state_dict(
torch.load(hf_hub_download("braindecode/plot_dance_event_detection", "model.pt"))
)
```
## Training
- Data: BI2014a, subjects 1, 2, 4–9 for training; subject 3 held out.
- Preprocessing (paper recipe): pick EEG, band-pass 0.1–100 Hz, resample to
128 Hz, per-channel robust scaling clamped to `[-16, 16]`.
- Windows: 32 s fixed length, up to 150 events/window, `num_latents = 256`.
- Optimiser: Adam, constant lr `5e-4`, batch size 16, 120 epochs,
best-by-held-out-F1-event checkpoint.
- Loss: `braindecode.training.DanceLoss` (matched-only IoU normalization).
Reproduce with `train_checkpoint.py` in this repository.
<!-- braindecode:training-data -->
## Training data
The data that shaped these weights. The ids are the `datasets:` field of this card,
which braindecode reads into `model.card_data.datasets` when you call
`from_pretrained` (MOABB class names for MOABB datasets, OpenNeuro / NEMAR
accessions, Hub ids otherwise). `nm`/`on` ids are datasets on [NEMAR](https://nemar.org)
(`on` = NEMAR's copy of the OpenNeuro dataset with the same number). Rows marked
`private:` are data the authors did not release; they are not in `datasets:`.
| `datasets:` id | Dataset | Stage | Modality |
|---|---|---|---|
| [`nm000244`](https://nemar.org/dataset/nm000244) | [Brain Invaders 2014a P300 dataset (MOABB BI2014a)](https://moabb.neurotechx.com/docs/generated/moabb.datasets.BI2014a.html) | training | EEG |
- Supervised DANCE event detection, cross-subject. Gradient updates on BI2014a subjects 1, 2, 4, 5, 6, 7, 8, 9 (all their sessions as loaded by MOABBDataset); subject 3 is the held-out subject but its F1-event is used to keep the best epoch (torch.save on improvement), so subject 3 selected the checkpoint. 32 s windows at 128 Hz, 120 epochs per card (script default 100).
- Sources: https://huggingface.co/braindecode/plot_dance_event_detection/resolve/main/train_checkpoint.py; W/cards-orig/plot_dance_event_detection.md; W/bd/examples/applied_examples/plot_dance_event_detection.py.
## Cite Braindecode
If you use this braindecode port in scientific work, cite the software on Zenodo,
[doi:10.5281/zenodo.17699192](https://doi.org/10.5281/zenodo.17699192),
and the original model and datasets cited above.
```bibtex
@software{braindecode,
author = {Aristimunha, Bruno and
Guetschel, Pierre and
Wimpff, Martin and
Gemein, Lukas and
Rommel, Cedric and
Banville, Hubert and
Sliwowski, Maciej and
Wilson, Daniel and
Brandt, Simon and
Gnassounou, Théo and
Paillard, Joseph and
{Junqueira Lopes}, Bruna and
Sedlar, Sara and
Moreau, Thomas and
Chevallier, Sylvain and
Gramfort, Alexandre and
Schirrmeister, Robin Tibor},
title = {Braindecode: toolbox for decoding raw electrophysiological brain data
with deep learning models},
url = {https://github.com/braindecode/braindecode},
doi = {10.5281/zenodo.17699192},
publisher = {Zenodo},
license = {BSD-3-Clause},
}
```
|