Recognition of motor imagery electroencephalography using independent component analysis and machine classifiers

Abstract

Motor imagery electroencephalography (EEG), which embodies cortical potentials during mental simulation of left or right finger lifting tasks, can be used as neural input signals to activate brain computer interface (BCI). The effectiveness of such an EEG-based BCI system relies on two indispensable features: distinguishable patterns of brain signals and accurate classifiers. This work aims to extract a reliable neural feature, termed as beta rebound map, out of motor imagery EEG by means of independent component analysis, and employ four classifiers to investigate the efficacy of beta rebound map. Results demonstrated that, with the use of ICA, the recognition rates of four classifiers, linear discriminant analysis (LDA), back-propagation neural network (BP-NN), radial-basis function neural network (RBF-NN), and support vector machine (SVM) improved significantly from 54%, 54%, 57.3% and 55% to 69.8.3%, 75.5%, 76.5% and 77.3%, respectively. In addition, the areas under the ROC curve, which assess the quality of classification over a wide range of misclassification costs, also improved greatly from .65, .60, .62, and .64 to .78, .73, .77 and .75, respectively.