发表机构
Southern University of Science and Technology; Capital Medical University（南方科技大学; 首都医科大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对基于EEG的听觉注意力切换解码存在的EEG非平稳性及性能高估问题，提出SGAD框架，经六个分层协议评估，该框架提升了解码准确性与稳定性且延迟低，推动了神经控制听觉应用的发展。
AI 中文摘要
实现鲁棒性基于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EEG非平稳性使序列决策复杂化，且对潜在混杂因素的控制不足可能高估性能，其应用受到限制。因此，我们提出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，并通过状态引导自适应门控动态调节时间平滑。我们进一步引入六个分层评估协议，以评估音频、说话者和受试者维度的泛化能力。实验结果表明，SGAD在所有评估场景中均提高了解码准确性和稳定性，同时保持低响应延迟。不同协议间的性能差异进一步表明存在与数据划分相关的偏差。这些发现共同推动了神经控制听觉应用中鲁棒性AASD的发展。
英文摘要
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.
