Adaboost for improving classification of left and right hand motor imagery tasks

Abstract

The Adaboost classifier with Fisher discriminant analysis (FDA) as base learner is proposed to discriminate the left and right hand motor imagery tasks in this paper. Firstly, multichannel complexity and held power of EEG within 10-12Hz over two brain hemispheres are extracted as feature vectors, which characterize the brain features during hand motor imagination. Then with the Adaboost classifier, the satisfactory classification results on test data can be obtained. The maximum classification accuracy reaches to 89.29% and the maximum mutual information is 0.59bit. The primary results show that the Adaboost could effectively improve the classification accuracy of left and right hand motor imagery tasks, so that it has great potentials to mental tasks classification for BCI.