DANCE: Detect and Classify Events in EEG
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866918494645256192 |
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| author | Lévy, Jarod Banville, Hubert Rapin, Jérémy King, Jean-Remi Moreau, Thomas d'Ascoli, Stéphane |
| author_facet | Lévy, Jarod Banville, Hubert Rapin, Jérémy King, Jean-Remi Moreau, Thomas d'Ascoli, Stéphane |
| contents | Event identification in continuous neural recordings is a critical task in neuroscience. Decoding in EEG is dominated by classifying windows aligned to known event onsets. However, while available in controlled experiments, such onsets are absent in continuous real-world monitoring. Here, we introduce DANCE, a deep learning pipeline that frames neural decoding as a set-prediction problem and jointly detects and classifies events directly from raw, unaligned signals. Evaluated separately on ten datasets curated from the literature with a wide variety of event types (ranging from milliseconds to minutes in duration), our model outperforms existing methods on a broad range of cognitive, clinical and BCI tasks. This single architecture establishes a new state of the art in the competitive task of seizure monitoring and matches the accuracy of onset-informed models for BCI tasks. Overall, our method marks a step towards end-to-end asynchronous neural decoding models |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_10688 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DANCE: Detect and Classify Events in EEG Lévy, Jarod Banville, Hubert Rapin, Jérémy King, Jean-Remi Moreau, Thomas d'Ascoli, Stéphane Machine Learning Signal Processing Event identification in continuous neural recordings is a critical task in neuroscience. Decoding in EEG is dominated by classifying windows aligned to known event onsets. However, while available in controlled experiments, such onsets are absent in continuous real-world monitoring. Here, we introduce DANCE, a deep learning pipeline that frames neural decoding as a set-prediction problem and jointly detects and classifies events directly from raw, unaligned signals. Evaluated separately on ten datasets curated from the literature with a wide variety of event types (ranging from milliseconds to minutes in duration), our model outperforms existing methods on a broad range of cognitive, clinical and BCI tasks. This single architecture establishes a new state of the art in the competitive task of seizure monitoring and matches the accuracy of onset-informed models for BCI tasks. Overall, our method marks a step towards end-to-end asynchronous neural decoding models |
| title | DANCE: Detect and Classify Events in EEG |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2605.10688 |