an efficient multiobjective feature optimization approach for improving motor imagery-based brain-computer interface performance

Background: applying efficient feature extraction and selection methods is essential in improving the performance of machine learning algorithms employed in brain-computer interface (bci) systems. objectives: the current study aims to enhance the performance of a motor imagery-based bci by improving the feature extraction and selection stages of the machine-learning algorithm applied to classify the different imagined movements. materials & methods: in this study, a multi-rate system for spectral decomposition of the signal is designed, and then the spatial and temporal features are extracted from each sub-band. to maximize the classification accuracy while simplifying the model and using the smallest set of features, the feature selection stage is treated as a multiobjective optimization problem, and the pareto optimal solutions of these two conflicting objectives are obtained. for the feature selection stage, non-dominated sorting genetic algorithm ii (nsga-ii), an evolutionary-based algorithm, is used wrapper-based, and its effect on the bci performance is explored. the proposed method is implemented on a public dataset known as bci competition iii dataset iva. results: extracting the spatial and temporal features from different sub-bands and selecting the features with an evolutionary optimization approach in this study led to an improved classification accuracy of 92.19% which has a higher value compared to the state of the art. conclusion: the results show that the proposed improved classification accuracy could achieve a high-performance subject-specific bci system.