通过实时感知用户注意力状态，动态过滤无效脑电信号，提升脑机接口稳定性。
Toward Adaptive BCIs: Enhancing Decoding Stability via User State-Aware EEG Filtering
- 根据脑电特征实时判断用户专注度，对信号进行自适应加权筛选。
- 在多个数据集上显著提升分类准确率与跨会话稳定性。
- 无需额外标注，仅用脑电信号即可增强实际脑机接口可靠性。
脑机接口（BCI）常因鲁棒性差、长期适应能力弱而受限。当用户注意力波动、脑状态随时间变化或交互中出现异常伪影时，模型性能迅速下降。为此，本文提出一种用户状态感知的脑电信号（EEG）滤波框架，在解码前优化神经表征。该方法持续从脑电特征中估计用户认知状态（如专注或分心），并依据估算的注意力水平对不可靠段落施加自适应权重过滤。该过滤阶段抑制噪声或非专注时段数据，有效缓解分布漂移，提升后续解码的一致性。在多个模拟真实场景的EEG数据集上实验表明，相比传统预处理流程，该状态感知滤波方法在不同用户状态和会话间均显著提升分类准确率与稳定性。结果表明，仅利用脑源性状态信息——即使无额外用户标签——也可大幅提升实际脑电基BCI的可靠性。
原文摘要 · Abstract (English)
Brain-computer interfaces (BCIs) often suffer from limited robustness and poor long-term adaptability. Model performance rapidly degrades when user attention fluctuates, brain states shift over time, or irregular artifacts appear during interaction. To mitigate these issues, we introduce a user state-aware electroencephalogram (EEG) filtering framework that refines neural representations before decoding user intentions. The proposed method continuously estimates the user's cognitive state (e.g., focus or distraction) from EEG features and filters unreliable segments by applying adaptive weighting based on the estimated attention level. This filtering stage suppresses noisy or out-of-focus epochs, thereby reducing distributional drift and improving the consistency of subsequent decoding. Experiments on multiple EEG datasets that emulate real BCI scenarios demonstrate that the proposed state-aware filtering enhances classification accuracy and stability across different user states and sessions compared with conventional preprocessing pipelines. These findings highlight that leveraging brain-derived state information--even without additional user labels--can substantially improve the reliability of practical EEG-based BCIs.
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