Recent advances in artificial intelligence demand an automated framework for the development of versatile brain–computer interface (BCI) systems. In this article, we proposed a novel automated framework that reveals the importance of multidomain features with feature selection to increase the performance of a learning algorithm for motor imagery electroencephalogram task classification on the utility of signal decomposition methods. A framework is explored by investigating several combinations of signal decomposition methods with feature selection techniques. Thus, this article also provides a comprehensive comparison among the aforementioned modalities and validates them with several performance measures, robust ranking, and statistical analysis (Wilcoxon and Friedman) on public benchmark databases. Among all the combinations, the variational mode decomposition, multidomain features obtained with linear regression, and the cascade-forward neural network provide better classification accuracy results for both subject-dependent and independent BCI systems in comparison with other state-of-the-art methods. Impact Statement—The brain–computer interface (BCI) is a revolutionary device that utilizes cognitive function explicitly for the interaction of external devices without any motor intervention. BCI systems based on motor imagery have shown efficacy for stroke patient treatment, but poor performance, nonflexible characteristics, and lengthy training sessions have limited their use in clinical practice. The proposed automated framework overcomes these limitations. With the significant improvement of up to 26.1% and 26.4% in comparison with the available literature, the proposed automated framework could offer help to BCI device developers to develop flexible BCI devices and provide interaction for motor-disabled users.