Improving Classification of Slow Cortical Potential Signals for BCI Systems With Polynomial Fitting and Voting Support Vector Machine

Abstract

Classification of slow cortical potential (SCP) signals is crucial for brain-computer interface (BCI) systems. This letter presents a new scheme to improve the classification performance of SCP signals. It consists of two parts: first, by fitting the wavelet coefficients of SCP signals with a second-order polynomial, the SCP trends are extracted; and second, a voting system based on the optimal training parameters of the support vector machines is developed to enhance the classification accuracy (CA). Experimental results reveal that the proposed scheme outperforms the state-of-the-art methods. The CA improvements for the dataset Ia of the BCI competition II and the TJU dataset (the dataset was collected in Tianjin University, termed TJU dataset) are reported.