提出自适应决策框架，提升脑电听觉注意力切换的鲁棒性。
SGAD: A State-Guided Adaptive Decision Framework for Robust EEG-Based Auditory Attention Switch Decoding
- 通过因果状态检测推断注意力转换状态，动态调节时间平滑。
- 在多维度评估中实现高精度与低延迟，稳定表现优于基准方法。
- 适合神经接口驱动助听器研发者，尤其关注实际场景泛化能力。
实现稳健的基于脑电图（EEG）的听觉注意力切换解码（AASD）对智能助听器至关重要。然而，由于脑电信号非平稳性导致序列决策困难，且潜在混杂因素控制不足可能夸大性能表现，限制了其应用。为此，我们提出一种状态引导自适应决策（SGAD）框架：通过因果状态检测推断注意力转换状态，并利用状态引导自适应门控动态调节时间平滑。此外，我们设计了六种分层评估协议，用于评估跨音频、说话人和被试维度的泛化能力。实验结果表明，SGAD在各类评估场景下均提升了解码准确率与稳定性，同时保持低响应延迟。不同评估协议间的性能差异进一步揭示了数据划分相关的偏差问题。这些发现推动了神经控制型听觉辅助系统中稳健AASD的发展。
原文摘要 · Abstract (English)
Achieving robust EEG-based auditory attention switch decoding (AASD) is crucial for intelligent hearing aids. However, its application is limited as EEG non-stationarity complicates sequential decision-making, and insufficient control of potential confounding factors may overestimate performance. Therefore, we propose a state-guided adaptive decision (SGAD) framework that infers attention transition states via causal state detection and dynamically modulates temporal smoothing through state-guided adaptive gating. We further introduce six hierarchical evaluation protocols to assess generalization across audio, speaker, and subject dimensions. Experimental results show that SGAD improves decoding accuracy and stability while maintaining low response latency across evaluation scenarios. Performance variations across protocols further suggest data partition-related biases. Together, these findings advance robust AASD for neuro-steered hearing applications.
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