The common spatial patterns (CSP) algorithm is one of the most frequently used and effective spatial filtering methods for extracting relevant features for use in motor imagery brain–computer interfaces (MI-BCIs). However, the inherent defect of the traditional CSP algorithm is that it is highly sensitive to potential outliers, which adversely affects its performance in practical applications. In this work, we propose a novel feature optimization and outlier detection method for the CSP algorithm. Specifically, we use the minimum covariance determinant (MCD) to detect and remove outliers in the dataset, then we use the Fisher score to evaluate and select features. In addition, in order to prevent the emergence of new outliers, we propose an iterative minimum covariance determinant (IMCD) algorithm. We evaluate our proposed algorithm in terms of iteration times, classification accuracy and feature distribution using two BCI competition datasets. The experimental results show that the average classification performance of our proposed method is 12% and 22.9% higher than that of the traditional CSP method in two datasets (p < 0.05), and our proposed method obtains better performance in comparison with other competing methods. The results show that our method improves the performance of MI-BCI systems.

中文翻译：

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基于迭代最小协方差行列式的运动意象脑机接口模型训练优化

通用空间模式 (CSP) 算法是最常用和最有效的空间滤波方法之一，用于提取用于运动想象脑机接口 (MI-BCI) 的相关特征。然而，传统CSP算法的固有缺陷是对潜在异常值高度敏感，这对其在实际应用中的性能产生了不利影响。在这项工作中，我们为 CSP 算法提出了一种新颖的特征优化和异常值检测方法。具体来说，我们使用最小协方差行列式 (MCD) 来检测和去除数据集中的异常值，然后我们使用 Fisher 分数来评估和选择特征。此外，为了防止出现新的异常值，我们提出了一种迭代最小协方差行列式（IMCD）算法。我们使用两个 BCI 竞争数据集在迭代时间、分类精度和特征分布方面评估我们提出的算法。实验结果表明，我们提出的方法在两个数据集上的平均分类性能分别比传统 CSP 方法提高了 12% 和 22.9%（p < 0.05)，与其他竞争方法相比，我们提出的方法获得了更好的性能。结果表明，我们的方法提高了 MI-BCI 系统的性能。