学习动态神经证据表示用于时间自适应脑机接口
Learning Dynamic Neural Evidence Representations for Time-Adaptive Brain-Computer Interfaces
- Australian AI Institute（澳大利亚人工智能研究所）
- University of Technology Sydney（悉尼科技大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对脑机接口固定窗口解码的局限，提出基于原型学习的EEG状态编码器ProtoTrigger，通过原型匹配与注意力聚合时间证据，实现自适应时间决策，在多个范式上取得最优权衡并验证实时可行性。
AI中文摘要：
脑机接口（BCI）将神经活动解码为命令，然而大多数现有系统依赖固定窗口解码，这可能因证据不足而导致冗余观测或不可靠的预测。自适应时间决策（ATDM）通过逐步累积脑电图（EEG）证据并决定何时停止，来解决这一准确性与时间之间的权衡。然而，现有的EEG编码器主要针对固定窗口解码设计，在可变观测长度下可能无法提供可靠的状态表示。此外，当前面向ATDM的编码器通常针对特定EEG范式定制，限制了其在不同BCI任务中的适用性。为解决这些局限，我们提出了ProtoTrigger，一种用于ATDM的两阶段基于原型学习的EEG状态编码器。ProtoTrigger利用原型匹配提取稳定的局部EEG嵌入，并在渐进观测过程中通过基于原型的注意力聚合与决策相关的时间证据。跨三个EEG范式的离线评估展示了最先进的准确性与时间权衡以及跨不同EEG范式的强泛化能力。一项基于增强现实的在线人在回路BCI实验进一步证明了其实时可行性。这些结果表明，ProtoTrigger为基于ATDM的高效BCI系统提供了一种通用的EEG状态编码框架。
英文摘要：
Brain-computer interfaces (BCIs) decode neural activity into commands, yet most existing systems rely on fixed-window decoding that may result in redundant observation or unreliable predictions due to insufficient evidence. Adaptive temporal decision-making (ATDM) addresses this accuracy-time trade-off by progressively accumulating EEG evidence and deciding when to stop. However, existing EEG encoders are mainly designed for fixed-window decoding and may not provide reliable state representations under variable observation lengths. In addition, current ATDM-oriented encoders are typically tailored to specific EEG paradigms, limiting their applicability across different BCI tasks. To address these limitations, we propose ProtoTrigger, a two-stage prototype learning-based EEG state encoder for ATDM. ProtoTrigger uses prototype matching to extract stable local EEG embeddings and prototype-based attention to aggregate decision-relevant temporal evidence during progressive observation. Offline evaluations across three EEG paradigms demonstrated state-of-the-art accuracy-time trade-offs and strong generalizability across different EEG paradigms. An online human-in-the-loop augmented reality-based BCI experiment further demonstrated its real-time feasibility. These results suggest that ProtoTrigger provides a general EEG state encoding framework for efficient ATDM-based BCI systems.
