Automated Recognition of Imagined Commands From EEG Signals Using Multivariate Fast and Adaptive Empirical Mode Decomposition Based Method

Abstract

In this letter, a novel automated approach for recognizing imagined commands using multichannel electroencephalogram (MEEG) signals is presented. The multivariate fast and adaptive empirical mode decomposition method decomposes the MEEG signals into various modes. The slope domain entropy and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_1$</tex-math></inline-formula> -norm features are obtained from the modes of MEEG signals. The machine learning models such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor, sparse representation classifier, and dictionary learning (DL) techniques are used for the imagined command classification tasks. The efficacy of the proposed approach is evaluated using MEEG from a public database as input signals. The proposed approach has achieved average accuracy values of 60.72, 59.73, and 58.78% using a DL model and selected features for left versus right, up versus down, forward versus backward based imagined command categorization tasks.