RCTP: Regularized common tensor pattern for rapid serial visual presentation spellers

Abstract

Common Spatial Pattern (CSP) is a powerful feature extraction method in brain-computer interface (BCI) systems. However, the CSP method has some deficiencies that limit its beneficiary. First, this method is not useful when data is noisy, and it is necessary to have a large dataset because CSP is inclined to overfit. Second, the CSP method uses just the spatial information of the data, and it cannot incorporate the temporal and spectral information. In this paper, we propose a new CSP-based algorithm which is capable of employing the information in all dimensions of data. Also, by defining the regularization term for each mode of information, we can diminish the noise effects and overfitting aspects. We design a simple mathematical framework (called RCTP) to obtain multiple filters of each subspace of information simultaneously. We evaluated our method on 6 subject's data recorded in a Rapid Serial Presentation (RSVP) speller paradigm. The average accuracy of 91.7% and 90.2% is achieved for RCTP and RBCSP methods, respectively. By comparing the obtained results with those of the conventional CSP, it can be shown that the average test accuracy achieved by the proposed RCTP method is 32.1% higher than that of the conventional CSP method. The proposed method can achieve high classification accuracy by defining the regularization terms and using all information of the data.