Investigation of Regularization Theory for Four-Class Classification in Brain-Computer Interface

Abstract

Common spatial patterns (CSP) is one of the most prevalent feature extraction approaches that has been used in Brain-computer interfaces (BCI) due to its simplicity and efficiency. Nevertheless, CSP suffers from the problems of sensitivity to noise and overfitting. To overcome these issues, the regularized CSP (RCSP) has been proposed recently. In addition, CSP was originally designed for two-class classification. However, a practical BCI usually needs four-class commands to be able to operate. Thus, there is a high demand for increasing the performance of multi-class BCI. In this paper, we provide a complete study of classification accuracy in multi-class BCI using regularization theory, and compare it with the standard CSP to determine the suitable method for feature extraction in BCI learning. Besides CSP, linear discriminant analysis (LDA) has shown its robust and widespread use for machine learning in BCI. LDA estimates covariance matrices from extracted features. But for high-dimensional features with only a small amount of training data given, the estimation may become imprecise. In the attempt of clarifying the regularizing effects in BCI, this paper also provides the classification results of the regularized LDA (RLDA). The performance evaluation of this work was taken on data from 9 subjects, from BCI competition datasets. Results show that the combination of standard CSP and LDA has a slightly better accuracy than the regularizing methods.