IEEE Trans Neural Netw Learn Syst. 2021 Nov;32(11):4814-4825. doi: 10.1109/TNNLS.2020.3015505. Epub 2021 Oct 27.
The common spatial pattern (CSP) algorithm is a well-recognized spatial filtering method for feature extraction in motor imagery (MI)-based brain-computer interfaces (BCIs). However, due to the influence of nonstationary in electroencephalography (EEG) and inherent defects of the CSP objective function, the spatial filters, and their corresponding features are not necessarily optimal in the feature space used within CSP. In this work, we design a new feature selection method to address this issue by selecting features based on an improved objective function. Especially, improvements are made in suppressing outliers and discovering features with larger interclass distances. Moreover, a fusion algorithm based on the Dempster-Shafer theory is proposed, which takes into consideration the distribution of features. With two competition data sets, we first evaluate the performance of the improved objective functions in terms of classification accuracy, feature distribution, and embeddability. Then, a comparison with other feature selection methods is carried out in both accuracy and computational time. Experimental results show that the proposed methods consume less additional computational cost and result in a significant increase in the performance of MI-based BCI systems.
共同空间模式（CSP）算法是一种广泛认可的运动想象（MI）脑机接口（BCI）特征提取的空间滤波方法。然而，由于脑电图（EEG）的非平稳性和 CSP 目标函数的固有缺陷的影响，在 CSP 中使用的特征空间内，空间滤波器及其对应的特征不一定是最优的。在这项工作中，我们设计了一种新的特征选择方法，通过基于改进的目标函数选择特征来解决这个问题。特别是，在抑制离群值和发现具有更大类间距离的特征方面进行了改进。此外，还提出了一种基于 Dempster-Shafer 理论的融合算法，该算法考虑了特征的分布。通过两个竞争数据集，我们首先根据分类准确性、特征分布和可嵌入性来评估改进的目标函数的性能。然后，在准确性和计算时间方面与其他特征选择方法进行了比较。实验结果表明，所提出的方法消耗的额外计算成本更少，并且显著提高了基于 MI 的 BCI 系统的性能。
