Classification of movement-related cortical potentials for multi-command control based on brain-machine interface

Abstract

Decoding of various motor intentions for generating command is one of the important factors in brain-based wheelchair system. The goal of this study focuses on classifying four types of trunk-related motor execution and imagery intentions. By brain components which are related to the trunk-related movements (waist, shoulder, and trunk) are generated in the very small and very close brain areas; therefore, decoding of trunk-related motor intentions are not easy for providing reliable system commands. To the best of our knowledge, the problems mentioned above have not been explored in the literature. In this study, we first validated the decoding accuracy of trunk-related motor intention based movement-related cortical potential. A set of binary classification performance which are shoulder extension (SE), waist rotation (WR), trunk flexion (TF), and rest (RE) have validated in respect to execution movement as well as imagery movement across six subjects. All binary classification results showed performance that is higher than the chance level. The best decoding accuracy shows 68.5% in the motor imagery task of shoulder extension vs. waist rotation.