Adaptive Radial Basis Functions Neural Network For Motor Imagery Task Classification

Abstract

Classification of Electroencephalogram (EEG) signals for motor imagery task has been a challenge for researchers due to the complex and highly non-separable structure of the data across different thought classes. For each specific thought in human brain, the EEG signals display a nonstationary behavior. Despite the non-similarity of EEG patterns within a motor imagery class, it is observed that they display some similarity across few samples. In this study, the similar behavior of training patterns of a motor imagery task is captured and patterns are grouped together to form sub-clusters. The sub-cluster centers are obtained using an evolutionary algorithm inspired by the attractiveness of the fireflies. Radial basis functions neural networks, with the sub-cluster centers thus obtained are used for classification. In this study, the convergence of the algorithm is analyzed for BCI Competition IV 2A data set and classification of the two motor imagery classes, left hand and tongue movement, is investigated.