Removing unrelated features based on linear dynamical system for motor-imagery-based brain-computer interface

Abstract

Common spatial pattern (CSP) is very successful in constructing spatial filters for detecting event-related synchronization and event-related desynchronization. In statistics, a CSP filter can optimally separate the motor-imagery-related components. However, for a single trail, the EEG features extracted after a CSP filter still include features not related to motor imagery. In this study, we introduce a linear dynamical system (LDS) approach to motor-imagery-based brain-computer interface (MI-BCI) to reduce the influence of these unrelated EEG features. This study is conducted on a BCI competition data set, which comprises EEG signals from several subjects performing various movements. Experimental results show that our proposed algorithm with LDS performs better than a traditional algorithm on average. The results reveal a promising direction in the application of LDS-based approach to MI-BCI. © 2011 Springer-Verlag.