File size: 2,170 Bytes
6c7cd4e | 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 | ---
license: mit
library_name: braindecode
tags:
- eeg
- event-detection
- dance
- p300
datasets:
- BI2014a
---
# 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.
|