Class discrepancy-guided sub-band filter-based common spatial pattern for motor imagery classification

摘要

Background: Motor imagery classification, an important branch of brain-computer interface (BCI), recognizes the intention of subjects to control external auxiliary equipment. Therefore, EEG-based motor imagery classification has received increasing attention in the fields of neuroscience. The common spatial pattern (CSP) algorithm has recently achieved great success in motor imagery classification. However, varying discriminative frequency bands and few-channel EEG limit the performance of CSP.