Multivariate variational mode decomposition & phase space reconstruction based motor imagery EEG classification

Abstract

Electroencephalogram signals have gained much attention in recent years as these signals help in decoding a person's intentions for performing various actions using the Brain Computer Interface system. However, it is challenging to accurately classify human intentions as the electroencephalogram signals are non-linear and non-stationary. This work proposes a novel methodology for feature extraction and classification of motor imagery electroencephalogram signals. The proposed methodology is based on Multivariate Variational Mode Decomposition and Phase Space Reconstruction. A number of statistical and non-linear features are extracted and classified using the Ensemble Support Vector Machine model. The efficiency of the methodology is evaluated on BCI competition-IV(2a) dataset. The evaluation results conclude that the proposed methodology is able to classify both binary and multi-class electroencephalogram data with an average classification accuracy of 96.76% and 84.22%, respectively. Hence, the proposed methodology can be utilized in designing a real-time Brain Computer interface system.