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.

中文翻译：

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使用多级粒子群优化的基于运动图像的BCI系统的通道和特征选择。

脑机接口（BCI）是连接人脑与计算机或其他电子设备的通信和控制系统。但是，与任务无关的无关渠道和误导性功能会限制分类性能。为了解决这些问题，我们提出了一种基于粒子群优化（PSO）的高效信号处理框架，用于通道和特征选择，通道选择和特征选择。改进的Stockwell变换用于特征提取，多级混合PSO-贝叶斯线性判别分析用于优化和分类。BCI竞赛III数据集I在这里用于确认所提出方案的优越性。与没有优化的方法（精度为89％）相比，基于PSO的方案的最佳分类精度在小于10时为99％。F分数分别为0.98和98.99％，并且信噪比更好，从而优于现有算法。结果表明，信道和特征选择方案可以加快收敛速度​​，达到全局最优，并减少训练时间。该框架可以显着提高分类性能，有效减少特征数量，大大缩短测试时间，可为相关的实时BCI应用系统研究提供参考。