Multifeature Analysis in Motor Imagery EEG Classification

Classification of EEG signals is core issues on EEG-based brain-computer interface (BCI). Typically, such classification has been performed using features extracted from EEG signals. Many features have proved to be unique enough to used in BCI application. However, different features show different discriminative power for different subjects or different trials. In this paper, multifeature was used to improve the system performance.