The right and left hand Motor Imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchical feature selection and classification for discrimination of right and left hand MI task. TE is calculated among EEG channels as the distinctive, effective connectivity features. TE is a model-free method that can measure nonlinear effective connectivity and analyze multivariate dependent directed information flow among neural EEG channels. Then four feature subset selection methods namely Relief-F, Fisher, Laplacian and local learning based clustering (LLCFS) algorithms are used to choose the most significant effective connectivity features and reduce redundant information. Finally, support vector machine (SVM) and LDA methods are used for classification. Results show that the best performance in 29 healthy subjects and 60 trials is achieved using the TE method via Relief-F algorithm as feature selection and SVM classification with 91.02% accuracy. Consequently, TE index and a hierarchical feature selection and classification could be useful for discrimination of right and left hand MI task from multichannel EEG signal.

Keywords: Electroencephalogram (EEG), Motor Imagery, Effective Connectivity, Transfer Entropy

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