An Efficient Multiobjective Feature Optimization Approach for Improving Motor Imagery-based Brain-computer Interface Performance
- سال انتشار: 1402
- محل انتشار: علوم اعصاب کاسپین، دوره: 10، شماره: 1
- کد COI اختصاصی: JR_CJNS-10-1_008
- زبان مقاله: انگلیسی
- تعداد مشاهده: 141
نویسندگان
Department of Mechanical Engineering, Faculty of Mechanical Engineering,, University of Guilan, Rasht, Iran.
Department of Dynamics, Control and Vibration, Faculty of Mechanical Engineering, University of Guilan, Rasht, Iran.
چکیده
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 ۹۲.۱۹% 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.کلیدواژه ها
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