DANCE: Detect and Classify Events in EEG

• URL: http://arxiv.org/abs/2605.10688v1
• Date: Mon, 11 May 2026 15:01:17 GMT
• Title: DANCE: Detect and Classify Events in EEG
• Abstract summary: We introduce DANCE, a deep learning pipeline that frames neural decoding as a set-prediction problem.<n>Our model outperforms existing methods on a broad range of cognitive, clinical and BCI tasks.<n>Overall, our method marks a step towards end-to-end asynchronous neural decoding models.
• Score: 10.621387184936703
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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