The brain-computer interface (BCI) enables individuals with severe physical impairments to communicate with the world. BCIs offer computational neuroscience opportunities and challenges in converting real-time brain activities to computer commands and are typically framed as a classification problem. This article focuses on the P300 BCI that uses the event-related potential (ERP) BCI design, where the primary challenge is classifying target/non-target stimuli. We develop a novel Gaussian latent group model with sparse time-varying effects (GLASS) for making Bayesian inferences on the P300 BCI. GLASS adopts a multinomial regression framework that directly addresses the dataset imbalance in BCI applications. The prior specifications facilitate i) feature selection and noise reduction using soft-thresholding, ii) smoothing of the time-varying effects using global shrinkage, and iii) clustering of latent groups to alleviate high spatial correlations of EEG data. 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. The application of GLASS identifies important EEG channels (PO8, Oz, PO7, Pz, C3) that align with existing literature. GLASS further reveals a group effect from channels in the parieto-occipital region (PO8, Oz, PO7), which is validated in cross-participant analysis.

翻译：脑机接口（BCI）使严重身体障碍患者能够与外界进行交流。BCI为计算神经科学提供了将实时大脑活动转换为计算机指令的机遇与挑战，通常被形式化为分类问题。本文聚焦于采用事件相关电位（ERP）BCI设计的P300 BCI，其主要挑战是对目标/非目标刺激进行分类。我们开发了一种新颖的高斯潜变量组模型（GLASS），用于对P300 BCI进行贝叶斯推理。GLASS采用多项逻辑回归框架，直接应对BCI应用中的数据集不平衡问题。先验设定有助于：i）通过软阈值方法实现特征选择与噪声抑制，ii）通过全局收缩实现时变效应的平滑处理，iii）潜变量组聚类以缓解脑电数据的高空间相关性。我们开发了高效的基于梯度的变分推理（GBVI）算法用于后验计算，并提供了用户友好的Python模块（详见https://github.com/BangyaoZhao/GLASS）。GLASS的应用识别出与现有文献一致的重要脑电通道（PO8、Oz、PO7、Pz、C3），并进一步揭示了顶枕区通道（PO8、Oz、PO7）的组效应，该效应在跨被试分析中得到验证。