A novel automated approach using multivariate fast and adaptive empirical mode decomposition and dictionary learning achieved approximately 60% accuracy in classifying imagined commands from EEG signals.
May aid BCI development for command recognition; leaves open validation before clinical use.
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 andL₁-norm features are obtained from the modes of MEEG signals. The machine learning models such ask-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.
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Dash et al. (2022) studied this question.
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