Wang Yubo, Veluvolu Kalyana C
School of Life Science and Technology, Xidian UniversityXi'an, China; School of Electronics Engineering, College of IT Engineering, Kyungpook National UniversityDaegu, South Korea.
School of Electronics Engineering, College of IT Engineering, Kyungpook National University Daegu, South Korea.
Front Neurosci. 2017 Feb 1;11:28. doi: 10.3389/fnins.2017.00028. eCollection 2017.
The most BCI systems that rely on EEG signals employ Fourier based methods for time-frequency decomposition for feature extraction. The band-limited multiple Fourier linear combiner is well-suited for such band-limited signals due to its real-time applicability. Despite the improved performance of these techniques in two channel settings, its application in multiple-channel EEG is not straightforward and challenging. As more channels are available, a spatial filter will be required to eliminate the noise and preserve the required useful information. Moreover, multiple-channel EEG also adds the high dimensionality to the frequency feature space. Feature selection will be required to stabilize the performance of the classifier. In this paper, we develop a new method based on Evolutionary Algorithm (EA) to solve these two problems simultaneously. The real-valued EA encodes both the spatial filter estimates and the feature selection into its solution and optimizes it with respect to the classification error. Three Fourier based designs are tested in this paper. Our results show that the combination of Fourier based method with covariance matrix adaptation evolution strategy (CMA-ES) has the best overall performance.
大多数依赖脑电图（EEG）信号的脑机接口（BCI）系统采用基于傅里叶变换的方法进行时频分解以提取特征。带限多重傅里叶线性组合器因其实时适用性而非常适合此类带限信号。尽管这些技术在双通道设置中的性能有所提高，但其在多通道脑电图中的应用并非直接且具有挑战性。随着可用通道数的增加，需要空间滤波器来消除噪声并保留所需的有用信息。此外，多通道脑电图还增加了频率特征空间的高维度。需要进行特征选择以稳定分类器的性能。在本文中，我们开发了一种基于进化算法（EA）的新方法来同时解决这两个问题。实值进化算法将空间滤波器估计和特征选择都编码到其解中，并针对分类误差对其进行优化。本文测试了三种基于傅里叶变换的设计。我们的结果表明，基于傅里叶变换的方法与协方差矩阵自适应进化策略（CMA-ES）的组合具有最佳的整体性能。
