Grouped Automatic Relevance Determination and Its Application in Channel Selection for P300 BCIs

During the development of a brain-computer interface, it is beneficial to exploit information in multiple electrode signals. However, a small channel subset is favored for not only machine learning feasibility, but also practicality in commercial and clinical BCI applications. An embedded channel selection approach based on grouped automatic relevance determination is proposed. The proposed Gaussian conjugate group-sparse prior and the embedded nature of the concerned Bayesian linear model enable simultaneous channel selection and feature classification. Moreover, with the marginal likelihood (evidence) maximization technique, hyper-parameters that determine the sparsity of the model are directly estimated from the training set, avoiding time-consuming cross-validation. Experiments have been conducted on P300 speller BCIs. The results for both public and in-house datasets show that the channels selected by our techniques yield competitive classification performance with the state-of-the-art and are biologically relevant to P300.

在脑机接口的开发过程中，利用多个电极信号中的信息是有益的。然而，一个小的通道子集不仅有利于机器学习的可行性，而且在商业和临床脑机接口应用中具有实用性。提出了一种基于分组自动相关性确定的嵌入式通道选择方法。所提出的高斯共轭组稀疏先验以及相关贝叶斯线性模型的嵌入式特性能够同时进行通道选择和特征分类。此外，利用边缘似然（证据）最大化技术，确定模型稀疏性的超参数可直接从训练集中估计出来，避免了耗时的交叉验证。在P300拼写器脑机接口上进行了实验。对公共数据集和内部数据集的结果表明，我们的技术所选择的通道产生了与现有技术相当的分类性能，并且与P300具有生物学相关性。