A Novel Ensemble Learning Approach for Classification of EEG Motor Imagery Signals

Brain-Computer Interfaces (BCI) based on Motor Imagery (MI) extract commands in real time and can be used to control a cursor, wheelchair, robot or prosthesis by performing only mental imaging tasks, such as imagining a movement of the right hand while the corresponding brain activity is measured and processed by the system. Because MI-based BCI offers a high degree of freedom, it helps people with motor disabilities communicate with the device by performing a sequence of MI tasks. Several techniques are being developed to improve the classification performance of the MI signals used in BCI. Most researches focused on improving methods for feature extraction and selection, but relied on linear classifiers for class prediction. In this paper, we investigate the use of ensemble learning methods to improve classification accuracy in a BCI paradigm based on 2-class MIs. We propose and compare eight combinations of classifiers on the BCI Competition III dataset IVb. The results obtained show that the combination of three classifiers: Radial Basis Function-Kernel Support Vector Machine (RBF-Kernel SVM), Linear Support Vector Machine (Linear SVM) and Decision Tree, gives the best value of kappa which is equal to 0.783. This combination can be successfully applied to BCI systems where the amount of data stems largely from daily recording.