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
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):
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.