IEEE J Biomed Health Inform. 2019 Sep;23(5):1990-2001. doi: 10.1109/JBHI.2018.2878048. Epub 2018 Oct 25.
This paper deals with the classification of steady-state visual evoked potentials (SSVEP), which is a multiclass classification problem addressed in SSVEP-based brain-computer interfaces. In particular, our method named MultiLRM_MKL uses multiple linear regression models under a Sparse Bayesian Learning (SBL) framework to discriminate between the SSVEP classes. The regression coefficients of each model are learned using the Variational Bayesian (VB) framework and the kernel trick is adopted not only for reducing the computational cost of our method, but also for enabling the combination of different kernel spaces. We verify the effectiveness of our method in handling different kernel spaces by evaluating its performance with a new kernel based on canonical correlation analysis. In particular, we prove the benefit of combining multiple kernels by outperforming several state-of-the-art methods in two SSVEP datasets, reaching an information transfer rate of 93 b/min using only three channels from the occipital area ( O, O, and O).
本文研究了稳态视觉诱发电位（SSVEP）的分类，这是基于 SSVEP 的脑机接口中解决的多类分类问题。具体来说，我们的方法名为 MultiLRM_MKL，它在稀疏贝叶斯学习（SBL）框架下使用多个线性回归模型来区分 SSVEP 类别。每个模型的回归系数都是使用变分贝叶斯（VB）框架学习的，并且采用核技巧不仅可以降低我们方法的计算成本，还可以实现不同核空间的组合。我们通过使用基于典型相关分析的新核来评估其性能，验证了我们的方法在处理不同核空间方面的有效性。特别是，我们通过在两个 SSVEP 数据集上优于几种最先进的方法，证明了组合多个核的好处，仅使用枕区（O、O 和 O）的三个通道就达到了 93 b/min 的信息传输率。
