Brain-computer interface (BCI) is a promising technology to help disabled people to interact with the world only through their brain signals. These systems are designed based on recognizing the patient’s intention by processing his brain signal measurements. The essence of Motor-Imagery (MI) BCI systems is to train a model which classifies the brain signals into several major motions using several training sessions. Current methods mainly employ common spatial patterns (CSP) algorithm to extract meaningful features from the brain signals. Since the CSP algorithm primarily is designed for a two-class paradigm, often data fusion techniques are used to combine the results of CSP-based binary classifiers in multi-class problems. Noting the critical drawback of current data fusion methods due to losing information when combining the results of binary classifiers, here we propose a novel method based on Dempster–Shafer theory to fuse the results of constituent binary classifiers. We applied the proposed method to the benchmark BCI competition iv set 2a data set. The results show a significant improvement, which prove success of our method in modeling uncertainty. More specifically, the average kappa value is 75% which is higher than other competing methods. The proposed method is general and applicable to a wide range of applications.
