Comparison of variational mode decomposition and empirical wavelet transform methods on EEG signals for motor imaginary applications

Abstract

Devising a reliable method for implementing brain computer interface (BCI) systems using electroencephalogram (EEG) signals is proposed. Applicability of two modal decomposition methods, variational mode decomposition (VMD) and empirical wavelet transform (EWT) on EEG signals for identifying the four different motor imaginary movements by the investigation of event-related desynchronisation (ERD) activity in the Mu-beta rhythm of EEG signals is analysed and compared. The EEG signals from each electrode corresponding to the sensorimotor cortex area of the brain are decomposed using VMD and EWT methods. Each decomposed modes are modelled using auto regressive (AR) modeling and feature vector is formed using the AR model parameters. On classification, better accuracy is perceived for VMD method in comparison with EWT and common spatial pattern (CSP) methods developed on the same dataset.