Electroencephalography (EEG)-based P300 brain-computer interfaces (BCIs) enable communication without physical movement by detecting stimulus-evoked neural responses. Accurate and efficient decoding remains challenging due to high dimensionality, temporal dependence, and complex interactions across EEG channels. Most existing approaches treat channels independently or rely on black-box machine learning models, limiting interpretability and personalization. We propose a sparse Bayesian time-varying regression framework that explicitly models pairwise EEG channel interactions while performing automatic temporal feature selection. The model employs a relaxed-thresholded Gaussian process prior to induce structured sparsity in both channel-specific and interaction effects, enabling interpretable identification of task-relevant channels and channel pairs. Applied to a publicly available P300 speller dataset of 55 participants, the proposed method achieves a median character-level accuracy of 100\% using all stimulus sequences and attains the highest overall decoding performance among competing statistical and deep learning approaches. Incorporating channel interactions yields subgroup-specific gains of up to 7\% in character-level accuracy, particularly among participants who abstained from alcohol (up to 18\% improvement). Importantly, the proposed method improves median BCI-Utility by approximately 10\% at its optimal operating point, achieving peak throughput after only seven stimulus sequences. These results demonstrate that explicitly modeling structured EEG channel interactions within a principled Bayesian framework enhances predictive accuracy, improves user-centric throughput, and supports personalization in P300 BCI systems.

翻译：基于脑电图（EEG）的P300脑机接口（BCI）通过检测刺激诱发的神经响应，实现无需肢体运动的通信。由于脑电信号的高维度特性、时间依赖性以及通道间复杂的相互作用，实现准确高效的解码仍具挑战。现有方法大多将各通道独立处理或依赖黑盒机器学习模型，限制了模型的可解释性与个性化能力。本文提出一种稀疏贝叶斯时变回归框架，该框架在实现自动时序特征选择的同时，显式建模脑电通道间的成对交互作用。模型采用松弛阈值高斯过程先验，在通道特异性效应与交互效应中引入结构化稀疏性，从而可解释地识别任务相关的通道及通道对。在包含55名参与者的公开P300拼写数据集上的实验表明：所提方法在使用全部刺激序列时达到100%的字符级中位准确率，并在统计方法与深度学习方法中取得最优的整体解码性能。引入通道交互建模为特定亚组带来最高达7%的字符级准确率提升（其中戒酒参与者群体提升幅度最高达18%）。尤为重要的是，该方法在其最优工作点将BCI效用中位数提升约10%，仅需七个刺激序列即可达到峰值信息传输率。这些结果表明：在严谨的贝叶斯框架内显式建模结构化的脑电通道交互，能够提升P300脑机接口系统的预测精度、改善以用户为中心的信息传输效率，并支持个性化适配。