Abstract
Brain-computer interface (BCI) is a communication and control system linking the human brain and computers or other electronic devices. However, irrelevant channels and misleading features unrelated to tasks limit classification performance. To address these problems, we propose an efficient signal processing framework based on particle swarm optimization (PSO) for channel and feature selection, channel selection, and feature selection. Modified Stockwell transforms were used for a feature extraction, and multilevel hybrid PSO-Bayesian linear discriminant analysis was applied to optimization and classification. The BCI Competition III dataset I was used here to confirm the superiority of the proposed scheme. Compared to a method without optimization (89% accuracy), the best classification accuracy of the PSO-based scheme was 99% when less than 10.5% of the original features were used, the test time was reduced by more than 90%, and it achieved Kappa values and F-score of 0.98 and 98.99%, respectively, and better signal-to-noise ratio, thereby outperforming existing algorithms. The results show that the channel and feature selection scheme can accelerate the speed of convergence to the global optimum and reduce the training time. As the proposed framework can significantly improve classification performance, effectively reduce the number of features, and greatly shorten the test time, it can serve as a reference for related real-time BCI application system research.
Highlights
Brain-computer interface (BCI) is a communication and control system linking the human brain and computers or other electronic devices
We propose an efficient signal processing framework based on particle swarm optimization (PSO) for channel and feature selection, channel selection, and feature selection
Modified Stockwell transforms were used for a feature extraction, and multilevel hybrid PSO-Bayesian linear discriminant analysis was applied to optimization and classification. e BCI Competition III dataset I was used here to confirm the superiority of the proposed scheme
Summary
Brain-computer interface (BCI) is a communication and control system linking the human brain and computers or other electronic devices. Irrelevant channels and misleading features unrelated to tasks limit classification performance To address these problems, we propose an efficient signal processing framework based on particle swarm optimization (PSO) for channel and feature selection, channel selection, and feature selection. Even if the best feature set can be identified, more training time is required To solve these technical problems, on the one hand, this study introduces an efficient optimization framework based on multilevel PSO (MLPSO). E scheme reduces the number of features and enhances the classification accuracy via selection of the best feature subsets that match the expected potential cortical activity patterns during the MI task, bringing it the advantages of a great range of application and easy implementation. MLPSO was used to optimize the task related to motor imagery in this study
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