A Boosting-based Approach to Multi-class EEG Classification for Brain Machine Interface

Abstract

This paper presents a boosting-based approach to multi-class classification for brain-machine interface. First, to classify multi kinds of image, we discuss a way to acquire a number of local frequency features from electroencephalogram (EEG) signals. Next, a new boosting-based approach to multi-class EEG classification is developed by utilizing local support vector machines according to the local frequency features. The utility of the proposed approach will be directly presented at the symposium.