DANCE直接从原始脑电数据中同步检测与分类事件，无需已知起始时间。
DANCE: Detect and Classify Events in EEG
- 将神经解码转化为集合预测问题，直接处理未对齐的原始信号。
- 在10个数据集上优于现有方法，癫痫监测达到新基准。
- 适合真实场景下的脑机接口和临床神经监测应用。
连续神经记录中的事件识别是神经科学中的关键任务。目前脑电图（EEG）解码主要依赖于与已知事件起始时间对齐的时间窗分类，但此类起始时间在真实世界持续监测中通常不可用。本文提出DANCE，一种深度学习流水线，将神经解码建模为集合预测问题，可直接从原始、未对齐信号中联合检测并分类事件。我们在来自文献的十个数据集上分别评估了该模型，涵盖从毫秒到分钟不等的多种事件类型。结果表明，该模型在广泛的认知、临床及脑机接口（BCI）任务中均超越现有方法，在癫痫监测任务上建立新的性能基准，并在BCI任务中达到与基于事件起始信息模型相当的准确率。整体而言，该方法标志着向端到端异步神经解码模型迈进的重要一步。
原文摘要 · Abstract (English)
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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