Filter bank riemannian-based kernel support vector machine for motor imagery decoding

Abstract

Brain computer interface (BCI) enables the communication between the brain and external machines through Electroencephalography (EEG) signals, which has attracted lots of attention. Motor Imagery-based BCI (MI-BCI) is one of the most important paradigms in the BCI field. In MI-BCI, machine learning algorithms can be employed for identifying the target limb of motor intention effectively. As a typical machine learning algorithm for motor imagery decoding, the Riemannian-based kernel support vector machine (RK-SVM) algorithm is not capable of feature extraction from multiple frequency bands, which limits its performance. To solve this problem, the Filter Bank Riemannian-based Kernel Support Vector Machine (FBRK-SVM) method that combines the filter bank structure and Riemannian-based kernel was proposed. In comparative experiments on two commonly used public datasets, it is found that the proposed algorithm can yield higher decoding performance, which provides a new option for the classification of motor imagery.

Keywords

• brain computer interface
• motor imagery
• riemannian geometry
• machine learning