A novel hybrid kernel function relevance vector machine for multi-task motor imagery EEG classification

Abstract

Relevance vector machine (RVM) is a sparse Bayesian probability model commonly utilized in classification problems. Kernel functions are critical for the classification capacity of RVM. The kernel functions of RVM are not limited by the Mercer theorem, which differs RVM from the traditional support vector machine (SVM). As a typical local kernel function, the Gaussian kernel function has strong interpolating capacity, while the polynomial kernel function, a representative of global kernel function, is good at extrapolation. By combining the Gaussian kernel function and the polynomial kernel function, this paper proposed a novel hybrid kernel function RVM which is effective for classification at both local and global feature levels. Multi-task motor imagery electroencephalogram (EEG) classification is considered to validate the proposed method. Firstly, the phase space reconstruction (PSR) is employed to project EEG data from the time domain into the high-dimensional phase space, where the phase space common spatial pattern (PSCSP) features are extracted by using the “one versus one” common spatial pattern (OVO-CSP) strategy. Then the obtained PSCSP features are utilized as the input feature vectors of the proposed hybrid kernel function RVM for classification. The experimental results show that the proposed method improves the accuracy and Kappa coefficient for the multi-task motor imagery EEG classification problem. The main contributions of the paper include the novel hybrid kernel function RVM and the PSCSP features extracted from EEG.