CaMBRAIN：基于因果状态空间模型的实时连续脑电图推理
CaMBRAIN: Real-time, Continuous EEG Inference with Causal State Space Models
AI总结：
提出首个基于因果Mamba的状态空间模型CaMBRAIN，通过多阶段自监督训练实现实时、长程连续的EEG信号推理，在三个数据集上达到SOTA且吞吐量提升10倍以上。
摘要：
脑电图（EEG）是一种监测脑电活动的关键非侵入性方法。EEG信号时长从几秒到数小时不等，给现有深度学习方法带来两大障碍：（1）现有EEG模型主要基于注意力机制，随着序列长度增加计算量呈二次增长；（2）由于固定长度输入要求，原始EEG信号必须以滑动窗口方式处理，阻碍了对整个信号的全局理解。为此，我们提出CaMBRAIN——首个基于因果Mamba的状态空间模型（SSM），能够实时推理EEG信号，并论证了考虑到EEG的因果单向性，双向方法是不必要的昂贵。然而，训练这样的模型并非易事，因为关键的EEG事件可能极其短暂（不到一秒），却被长达数分钟的间隔分隔。当前的EEG方法使用自监督目标优化信号重建，但这些方法不适用于流式SSM；它们未能明确训练隐藏状态以保留流式推理所需的关键长程上下文。因此，我们引入了一种专门设计的多阶段自监督训练流程，以鼓励长程记忆保持和在EEG信号上的强性能，同时保持状态空间模型的线性时间复杂度。CaMBRAIN在三个不同的EEG数据集上达到了最先进（SOTA）结果，吞吐量比现有模型高10倍以上，成为首个能够对可变长度EEG信号进行长程连续推理的模型。
英文摘要：
Electroencephalography (EEG) is a critical, non-invasive method to monitor electrical brain activity. EEGs can span anywhere from a couple seconds to multiple hours, posing a major hurdle for existing deep learning methods due to two major factors: (1) existing EEG models are predominantly built upon the attention mechanism, incurring quadratic scaling as the sequence length increases, and (2) raw EEG signals must be processed in a sliding-window fashion due to fixed-length input requirements, preventing global understanding of the entire signal. To this extent, we propose CaMBRAIN - the first Causal, Mamba-based state space model (SSM) capable of real-time inference of EEG signals, arguing that bidirectional approaches are needlessly expensive given the causal, unidirectional nature of EEG. However, training such a model is non-trivial, as crucial EEG events can be extremely brief - within fractions of a second - yet separated by long intervals spanning minutes. Current EEG methods use self-supervised objectives that optimize for signal reconstruction, but these are not well suited for streaming SSMs; they fail to explicitly train the hidden state to retain the salient long-range context needed for streaming inference. We therefore introduce a multi-stage self-supervised training pipeline specifically tailored to encourage long-range memory retention and strong performance on EEG signals, while preserving the linear-time complexity of state space models. CaMBRAIN achieves state-of-the-art (SOTA) results across 3 different EEG datasets with >10x higher throughput than existing models, enabling the first model capable of long-range, continuous inference of variable-length EEG signals.
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