首页|Studies from University of Guilan Provide New Data on Machine Learning (An Efficient Multiobjective Feature Optimization Ap- proach for Improving Motor Imagery-based Brain-computer Inter- face Performance)
Studies from University of Guilan Provide New Data on Machine Learning (An Efficient Multiobjective Feature Optimization Ap- proach for Improving Motor Imagery-based Brain-computer Inter- face Performance)
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2024 FEB 20 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Data detailed on artificial intelligence have been presented. According to news reporting originating from Rasht, Iran, by NewsRx correspondents, research stated, “Applying efficient feature extraction and selection methods is essential in improving the performance of machine learning algorithms employed in brain-computer interface (BCI) systems.” The news correspondents obtained a quote from the research from University of Guilan: “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. 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. 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.”
University of GuilanRashtIranAsiaAlgorithmsCyborgsEmerging TechnologiesMachine Learning
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