Subspace-constrained multilinear discriminant analysis for ERP-based brain computer interface classification

Classification in brain computer interfaces (BCIs) frequently suffers from small sample problem which leads an ill-posed problem and overfitting/overtraining. To avoid and reduce the problems, we propose a multilinear formed linear discriminant analysis with constraints which are derived from other datasets. The proposed method prevents the ill-posed problem by reducing variables and improves robustness by transferring information from other datasets. The experimental results show that the proposed method improves classification accuracy in event-related potential-based BCIs.