---
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},
}
```