Brain-computer interfaces (BCIs), particularly the P300 BCI, facilitate direct communication between the brain and computers. The fundamental statistical problem in P300 BCIs lies in classifying target and non-target stimuli based on electroencephalogram (EEG) signals. However, the low signal-to-noise ratio (SNR) and complex spatial/temporal correlations of EEG signals present challenges in modeling and computation, especially for individuals with severe physical disabilities-BCI's primary users. To address these challenges, we introduce a novel Gaussian Latent channel model with Sparse time-varying effects (GLASS) under a fully Bayesian framework. GLASS is built upon a constrained multinomial logistic regression particularly designed for the imbalanced target and non-target stimuli. The novel latent channel decomposition efficiently alleviates strong spatial correlations between EEG channels, while the soft-thresholded Gaussian process (STGP) prior ensures sparse and smooth time-varying effects. We demonstrate GLASS substantially improves BCI's performance in participants with amyotrophic lateral sclerosis (ALS) and identifies important EEG channels (PO8, Oz, PO7, and Pz) in parietal and occipital regions that align with existing literature. For broader accessibility, we develop an efficient gradient-based variational inference (GBVI) algorithm for posterior computation and provide a user-friendly Python module available at https://github.com/BangyaoZhao/GLASS.

翻译：脑机接口（BCI），特别是P300 BCI，实现了大脑与计算机间的直接通信。P300 BCI的核心统计问题在于基于脑电图（EEG）信号对目标刺激与非目标刺激进行分类。然而，EEG信号的低信噪比（SNR）以及复杂的时空相关性给建模和计算带来了挑战，尤其是对于BCI的主要用户——严重身体残疾患者。为应对这些挑战，我们提出了一种在全贝叶斯框架下的新型高斯潜变量通道稀疏时变效应模型（GLASS）。GLASS建立在针对不平衡目标与非目标刺激设计的约束多项逻辑回归基础上。其新颖的潜变量通道分解有效缓解了EEG通道间的强空间相关性，而软阈值高斯过程（STGP）先验则确保了稀疏且平滑的时变效应。我们证明，GLASS显著提升了肌萎缩侧索硬化症（ALS）患者使用BCI的性能，并识别出顶叶和枕叶区域的重要EEG通道（PO8、Oz、PO7和Pz），这与现有文献一致。为提升可及性，我们开发了一种高效的基于梯度的变分推断（GBVI）算法用于后验计算，并提供了用户友好的Python模块（下载地址：https://github.com/BangyaoZhao/GLASS）。