A Brain-Computer Interface (BCI) speller systems based on Event-Related Potentials (ERPs) enables users to select characters by detecting brain responses to visual stimuli, recorded through electroencephalogram (EEG). One challenge is to accurately identify target-related responses, such as the P300 component. However, existing methods tend to ignore feature selection, perform feature selection without interpretability, or require large computational effort or data manipulation. To address these limitations, we propose a novel Bayesian generative modeling framework to the binary classification of EEG responses to stimuli. Our approach employs a Probit-link Split-and-merge Gaussian Process (P-SMGP) prior to perform spatial-temporal feature selection, effectively capturing the distinctions between target and non-target ERP responses. Through both simulation studies and real EEG data analysis, our approach can reduce computational complexity and provide statistical interpretations on transformed ERP functions while maintaining comparable prediction accuracy. These findings underscore the value of interpretable, stimulus-level modeling for advancing predictive and personalized BCI systems.

翻译：基于事件相关电位的脑机接口拼写系统通过检测视觉刺激诱发的脑电图反应，使用户能够选择字符。其中一个挑战在于准确识别与目标相关的响应，如P300成分。然而，现有方法往往忽略特征选择、在缺乏可解释性的情况下进行特征选择，或需要大量计算或数据操作。为解决这些局限性，我们提出一种新颖的贝叶斯生成建模框架，用于脑电图响应刺激的二分类任务。该方法采用概率链接分裂合并高斯过程先验进行时空特征选择，有效捕捉目标与非目标事件相关电位响应之间的差异。通过仿真实验和真实脑电图数据分析，该方法在降低计算复杂度的同时，能够对变换后的事件相关电位函数提供统计解释，并保持相当的预测精度。这些发现强调了可解释的刺激级建模对于推进预测性和个性化脑机系统发展的价值。